Awesome work on the VAE, disentanglement, representation learning, and generative models.
I gathered these resources (currently @ 300 papers) as literature for my PhD, and thought it may come in useful for others. This list includes works relevant to various topics relating to VAEs. Sometimes this spills over to topics e.g. adversarial training and GANs, general disentanglement, variational inference, flow-based models and auto-regressive models. Always keen to expand the list. I have also included an excel file which includes notes on each paper, as well as a breakdown of the topics covered in each paper.
They are ordered by year (new to old). I provide a link to the paper as well as to the github repo where available.
Weakly supervised disentanglement with guarantees. Shu, Chen, Kumar, Ermon, Poole https://arxiv.org/pdf/1910.09772.pdf
Demystifying inter-class disentanglement. Gabbay, Hoshen https://arxiv.org/pdf/1906.11796.pdf
Spectral regularization for combating mode collapse in GANs. Liu, Tang, Xie, Qiu https://arxiv.org/pdf/1908.10999.pdf
Geometric disentanglement for generative latent shape models. Aumentado-Armstrong, Tsogkas, Jepson, Dickinson https://arxiv.org/pdf/1908.06386.pdf
Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation. Li, Lin, Lin, Wang https://arxiv.org/pdf/1909.09675.pdf
Identity from here, pose from there: self-supervised disentanglement and generation of objects using unlabeled videos. Xiao, Liu, Lee https://web.cs.ucdavis.edu/~yjlee/projects/iccv2019_disentangle.pdf
Content and style disentanglement for artistic style transfer. Kotovenko, Sanakoyeu, Lang, Ommer https://compvis.github.io/content-style-disentangled-ST/paper.pdf
Unsupervised robust disentangling of latent characteristics for image synthesis. Esser, Haux, Ommer https://arxiv.org/pdf/1910.10223.pdf
LADN: local adversarial disentangling network for facial makeup and de-makeup. Gu, Wang, Chiu, Tai, Tang https://arxiv.org/pdf/1904.11272.pdf
Video compression with rate-distortion autoencoders. Habibian, van Rozendaal, Tomczak, Cohen https://arxiv.org/pdf/1908.05717.pdf
Variable rate deep image compression with a conditional autoencoder. Choi, El-Khamy, Lee https://arxiv.org/pdf/1909.04802.pdf
Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. Gong, Liu, Le, Saha https://arxiv.org/pdf/1904.02639.pdf
AVT: unsupervise d learning of transformation equivariant representations by autoencoding variational transformations. Qi, Zhang, Chen, Tian https://arxiv.org/pdf/1903.10863.pdf
Deep clustering by Gaussian mixture variational autoencoders with graph embedding. Yang, Cheung, Li, Fang http://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_Deep_Clustering_by_Gaussian_Mixture_Variational_Autoencoders_With_Graph_Embedding_ICCV_2019_paper.pdf
Variational adversarial active learning. Sinha, Ebrahimi, Darrell https://arxiv.org/pdf/1904.00370.pdf
Variational few-shot learning. Zhang, Zhao, Ni, Xu, Yang http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Variational_Few-Shot_Learning_ICCV_2019_paper.pdf
Multi-angle point cloud-VAE: unsupervised feature learning for 3D point clouds from multiple angles by joint self-reconstruction and half-to-half prediction. Han, Wang, Liu, Zwicker https://arxiv.org/pdf/1907.12704.pdf
LayoutVAE: stochastic scene layout generation from a label set. Jyothi, Durand, He, Sigal, Mori https://arxiv.org/pdf/1907.10719.pdf
VV-NET: Voxel VAE Net with group convolutions for point cloud segmentation. Meng, Gao, Lai, Manocha https://arxiv.org/pdf/1811.04337.pdf
Bayes-Factor-VAE: hierarchical bayesian deep auto-encoder models for factor disentanglement. Kim, Wang, Sahu, Pavlovic https://arxiv.org/pdf/1909.02820.pdf
Robust ordinal VAE: Employing noisy pairwise comparisons for disentanglement. Chen, Batmanghelich https://arxiv.org/pdf/1910.05898.pdf
Evaluating disentangled representations. Sepliarskaia, A. and Kiseleva, J. and de Rijke, M. https://arxiv.org/pdf/1910.05587.pdf
A stable variational autoencoder for text modelling. Li, R. and Li, X. and Lin, C. and Collinson, M. and Mao, R. https://abdn.pure.elsevier.com/en/publications/a-stable-variational-autoencoder-for-text-modelling
Hamiltonian generative networks. Toth, Rezende, Jaegle, Racaniere, Botev, Higgins https://128.84.21.199/pdf/1909.13789.pdf
LAVAE: Disentangling location and appearance. Dittadi, Winther https://arxiv.org/pdf/1909.11813.pdf
Interpretable models in probabilistic machine learning. Kim https://ora.ox.ac.uk/objects/uuid:b238ed7d-7155-4860-960e-6227c7d688fb/download_file?file_format=pdf&safe_filename=PhD_Thesis_of_University_of_Oxford.pdf&type_of_work=Thesis
Disentangling speech and non-speech components for building robust acoustic models from found data. Gurunath, Rallabandi, Black https://arxiv.org/pdf/1909.11727.pdf
Joint separation, dereverberation and classification of multiple sources using multichannel variational autoencoder with auxiliary classifier. Inoue, Kameoka, Li, Makino http://pub.dega-akustik.de/ICA2019/data/articles/000906.pdf
SuperVAE: Superpixelwise variational autoencoder for salient object detection. Li, Sun, Guo https://www.aaai.org/ojs/index.php/AAAI/article/view/4876
Implicit discriminator in variational autoencoder. Munjal, Paul, Krishnan https://arxiv.org/pdf/1909.13062.pdf
TransGaGa: Geometry-aware unsupervised image-to-image translation. Wu, Cao, Li, Qian, Loy http://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_TransGaGa_Geometry-Aware_Unsupervised_Image-To-Image_Translation_CVPR_2019_paper.pdf
Variational attention using articulatory priors for generating code mixed speech using monolingual corpora. Rallabandi, Black. https://www.isca-speech.org/archive/Interspeech_2019/pdfs/1103.pdf
One-class collaborative filtering with the queryable variational autoencoder. Wu, Bouadjenek, Sanner. https://people.eng.unimelb.edu.au/mbouadjenek/papers/SIGIR_Short_2019.pdf
Predictive auxiliary variational autoencoder for representation learning of global speech characteristics. Springenberg, Lakomkin, Weber, Wermter. https://www.isca-speech.org/archive/Interspeech_2019/pdfs/2845.pdf
Data augmentation using variational autoencoder for embedding based speaker verification. Wu, Wang, Qian, Yu https://zhanghaowu.me/assets/VAE_Data_Augmentation_proceeding.pdf
One-shot voice conversion with disentangled representations by leveraging phonetic posteriograms. Mohammadi, Kim. https://www.isca-speech.org/archive/Interspeech_2019/pdfs/1798.pdf
EEG-based adaptive driver-vehicle interface using variational autoencoder and PI-TSVM. Bi, Zhang, Lian https://www.researchgate.net/profile/Luzheng_Bi2/publication/335619300_EEG-Based_Adaptive_Driver-Vehicle_Interface_Using_Variational_Autoencoder_and_PI-TSVM/links/5d70bb234585151ee49e5a30/EEG-Based-Adaptive-Driver-Vehicle-Interface-Using-Variational-Autoencoder-and-PI-TSVM.pdf
Neural gaussian copula for variational autoencoder Wang, Wang https://arxiv.org/pdf/1909.03569.pdf
Enhancing VAEs for collaborative filtering: Flexible priors and gating mechanisms. Kim, Suh http://delivery.acm.org/10.1145/3350000/3347015/p403-kim.pdf?ip=86.162.136.199&id=3347015&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1568726810_89cfa7cbc7c1b0663405d4446f9fce85
Riemannian normalizing flow on variational wasserstein autoencoder for text modeling. Wang, Wang https://arxiv.org/pdf/1904.02399.pdf
Disentanglement with hyperspherical latent spaces using diffusion variational autoencoders. Rey https://openreview.net/pdf?id=SylFDSU6Sr
Learning deep representations by mutual information estimation and maximization. Hjelm, Fedorov, Lavoie-Marchildon, Grewal, Bachman, Trischler, Bengio https://arxiv.org/pdf/1808.06670.pdf https://github.com/rdevon/DIM
Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation. Vladymyrov, Ariga https://arxiv.org/pdf/1909.04427.pdf
Real time trajectory prediction using conditional generative models. Gomez-Gonzalez, Prokudin, Scholkopf, Peters https://arxiv.org/pdf/1909.03895.pdf
Disentanglement challenge: from regularization to reconstruction. Qiao, Li, Cai https://openreview.net/pdf?id=ByecPrUaHH
Improved disentanglement through aggregated convolutional feature maps. Seitzer https://openreview.net/pdf?id=ryxOvH86SH
Linked variational autoencoders for inferring substitutable and supplementary items. Rakesh, Wang, Shu http://www.public.asu.edu/~skai2/files/wsdm_2019_lvae.pdf
On the fairness of disentangled representations. Locatello, Abbati, Rainforth, Bauer, Scholkopf, Bachem https://arxiv.org/pdf/1905.13662.pdf
Learning robust representations by projecting superficial statistics out. Wang, He, Lipton, Xing https://openreview.net/pdf?id=rJEjjoR9K7
Understanding posterior collapse in generative latent variable models. Lucas, Tucker, Grosse, Norouzi https://openreview.net/pdf?id=r1xaVLUYuE
On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset. Gondal, Wuthrich, Miladinovic, Locatello, Breidt, Volchkv, Akpo, Bachem, Scholkopf, Bauer https://arxiv.org/pdf/1906.03292.pdf https://github.com/rr-learning/disentanglement_dataset
DIVA: domain invariant variational autoencoder. Ilse, Tomczak, Louizos, Welling https://arxiv.org/pdf/1905.10427.pdf https://github.com/AMLab-Amsterdam/DIVA
Comment: Variational Autoencoders as empirical Bayes. Wang, Miller, Blei http://www.stat.columbia.edu/~yixinwang/papers/WangMillerBlei2019.pdf
Fast MVAE: joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier. Li, Kameoka, Makino https://ieeexplore.ieee.org/abstract/document/8682623
Reweighted expectation maximization. Dieng, Paisley https://arxiv.org/pdf/1906.05850.pdf https://github.com/adjidieng/REM
Semisupervised text classification by variational autoencoder. Xu, Tan https://ieeexplore.ieee.org/abstract/document/8672806
Learning deep latent-variable MRFs with amortized Bethe free-energy minimization. Wiseman https://openreview.net/pdf?id=ByeMHULt_N
Contrastive variational autoencoder enhances salient features. Abid, Zou https://arxiv.org/pdf/1902.04601.pdf https://github.com/abidlabs/contrastive_vae
Learning latent superstructures in variational autoencoders for deep multidimensional clustering. Li, Chen, Poon, Zhang https://openreview.net/pdf?id=SJgNwi09Km
Tighter variational bounds are not necessarily better. Rainforth, Kosiorek, Le, Maddison, Igl, Wood, The https://arxiv.org/pdf/1802.04537.pdf https://github.com/lxuechen/DReG-PyTorch
ISA-VAE: Independent subspace analysis with variational autoencoders. Anon. https://openreview.net/pdf?id=rJl_NhR9K7
Manifold mixup: better representations by interpolating hidden states. Verma, Lamb, Beckham, Najafi, Mitliagkas, Courville, Lopez-Paz, Bengio. https://arxiv.org/pdf/1806.05236.pdf https://github.com/vikasverma1077/manifold_mixup
Bit-swap: recursive bits-back coding for lossless compression with hierarchical latent variables. Kingma, Abbeel, Ho. http://proceedings.mlr.press/v97/kingma19a/kingma19a.pdf https://github.com/fhkingma/bitswap
Practical lossless compression with latent variables using bits back coding. Townsend, Bird, Barber. https://arxiv.org/pdf/1901.04866.pdf https://github.com/bits-back/bits-back
BIVA: a very deep hierarchy of latent variables for generative modeling. Maaloe, Fraccaro, Lievin, Winther. https://arxiv.org/pdf/1902.02102.pdf
Flow++: improving flow-based generative models with variational dequantization and architecture design. Ho, Chen, Srinivas, Duan, Abbeel. https://arxiv.org/pdf/1902.00275.pdf https://github.com/aravindsrinivas/flowpp
Sylvester normalizing flows for variational inference. van den Berg, Hasenclever, Tomczak, Welling. https://arxiv.org/pdf/1803.05649.pdf https://github.com/riannevdberg/sylvester-flows
Unbiased implicit variational inference. Titsias, Ruiz. https://arxiv.org/pdf/1808.02078.pdf
Robustly disentangled causal mechanisms: validating deep representations for interventional robustness. Suter, Miladinovic, Scholkopf, Bauer. https://arxiv.org/pdf/1811.00007.pdf
Tutorial: Deriving the standard variational autoencoder (VAE) loss function. Odaibo https://arxiv.org/pdf/1907.08956.pdf
Learning disentangled representations with reference-based variational autoencoders. Ruiz, Martinez, Binefa, Verbeek. https://arxiv.org/pdf/1901.08534
Disentangling factors of variation using few labels. Locatello, Tschannen, Bauer, Ratsch, Scholkopf, Bachem https://arxiv.org/pdf/1905.01258.pdf
Disentangling disentanglement in variational autoencoders Mathieu, Rainforth, Siddharth, The, https://arxiv.org/pdf/1812.02833.pdf https://github.com/iffsid/disentangling-disentanglement
LIA: latently invertible autoencoder with adversarial learning Zhu, Zhao, Zhang https://arxiv.org/pdf/1906.08090.pdf
Emerging disentanglement in auto-encoder based unsupervised image content transfer. Press, Galanti, Benaim, Wolf https://openreview.net/pdf?id=BylE1205Fm https://github.com/oripress/ContentDisentanglement
MAE: Mutual posterior-divergence regularization for variational autoencoders Ma, Zhou, Hovy https://arxiv.org/pdf/1901.01498.pdf https://github.com/XuezheMax/mae
Overcoming the disentanglement vs reconstruction trade-off via Jacobian supervision. Lezama https://openreview.net/pdf?id=Hkg4W2AcFm https://github.com/jlezama/disentangling-jacobian https://github.com/jlezama/disentangling-jacobian/tree/master/unsupervised_disentangling
Challenging common assumptions in the unsupervised learning of disentangled representations. Locatello, Bauer, Lucic, Ratsch, Gelly, Scholkopf, Bachem https://arxiv.org/abs/1811.12359 https://github.com/google-research/disentanglement_lib/blob/master/README.md
Variational prototyping encoder: one shot learning with prototypical images. Kim, Oh, Lee, Pan, Kweon http://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_Variational_Prototyping-Encoder_One-Shot_Learning_With_Prototypical_Images_CVPR_2019_paper.pdf
Diagnosing and enchanving VAE models (conf and journal paper both available). Dai, Wipf https://arxiv.org/pdf/1903.05789.pdf https://github.com/daib13/TwoStageVAE
Disentangling latent hands for image synthesis and pose estimation. Yang, Yao http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Disentangling_Latent_Hands_for_Image_Synthesis_and_Pose_Estimation_CVPR_2019_paper.pdf
Rare event detection using disentangled representation learning. Hamaguchi, Sakurada, Nakamura http://openaccess.thecvf.com/content_CVPR_2019/papers/Hamaguchi_Rare_Event_Detection_Using_Disentangled_Representation_Learning_CVPR_2019_paper.pdf
Disentangling latent space for VAE by label relevant/irrelvant dimensions. Zheng, Sun https://arxiv.org/pdf/1812.09502.pdf https://github.com/ZhilZheng/Lr-LiVAE
Variational autoencoders pursue PCA directions (by accident). Rolinek, Zietlow, Martius https://arxiv.org/pdf/1812.06775.pdf
Disentangled Representation learning for 3D face shape. Jiang, Wu, Chen, Zhang https://arxiv.org/pdf/1902.09887.pdf https://github.com/zihangJiang/DR-Learning-for-3D-Face
Preventing posterior collapse with delta-VAEs. Razavi, van den Oord, Poole, Vinyals https://arxiv.org/pdf/1901.03416.pdf https://github.com/mattjj/svae
Gait recognition via disentangled representation learning. Zhang, Tran, Yin, Atoum, Liu, Wan, Wang https://arxiv.org/pdf/1904.04925.pdf
Hierarchical disentanglement of discriminative latent features for zero-shot learning. Tong, Wang, Klinkigt, Kobayashi, Nonaka http://openaccess.thecvf.com/content_CVPR_2019/papers/Tong_Hierarchical_Disentanglement_of_Discriminative_Latent_Features_for_Zero-Shot_Learning_CVPR_2019_paper.pdf
Generalized zero- and few-shot learning via aligned variational autoencoders. Schonfeld, Ebrahimi, Sinha, Darrell, Akata https://arxiv.org/pdf/1812.01784.pdf https://github.com/chichilicious/Generalized-Zero-Shot-Learning-via-Aligned-Variational-Autoencoders
Unsupervised part-based disentangling of object shape and appearance. Lorenz, Bereska, Milbich, Ommer https://arxiv.org/pdf/1903.06946.pdf
A semi-supervised Deep generative model for human body analysis. de Bem, Ghosh, Ajanthan, Miksik, Siddaharth, Torr http://www.robots.ox.ac.uk/~tvg/publications/2018/W21P20.pdf
Multi-object representation learning with iterative variational inference. Greff, Kaufman, Kabra, Watters, Burgess, Zoran, Matthey, Botvinick, Lerchner https://arxiv.org/pdf/1903.00450.pdf https://github.com/MichaelKevinKelly/IODINE
Generating diverse high-fidelity images with VQ-VAE-2. Razavi, van den Oord, Vinyals https://arxiv.org/pdf/1906.00446.pdf https://github.com/deepmind/sonnet/blob/master/sonnet/examples/vqvae_example.ipynb https://github.com/rosinality/vq-vae-2-pytorch
MONet: unsupervised scene decomposition and representation. Burgess, Matthey, Watters, Kabra, Higgins, Botvinick, Lerchner https://arxiv.org/pdf/1901.11390.pdf
Structured disentangled representations and Hierarchical disentangled representations. Esmaeili, Wu, Jain, Bozkurt, Siddarth, Paige, Brooks, Dy, van de Meent https://arxiv.org/pdf/1804.02086.pdf
Spatial Broadcast Decoder: A simple architecture for learning disentangled representations in VAEs. Watters, Matthey, Burgess, Lerchner https://arxiv.org/pdf/1901.07017.pdf https://github.com/lukaszbinden/spatial-broadcast-decoder
Resampled priors for variational autoencoders. Bauer, Mnih https://arxiv.org/pdf/1802.06847.pdf
Weakly supervised disentanglement by pairwise similiarities. Chen, Batmanghelich https://arxiv.org/pdf/1906.01044.pdf
Deep variational information bottleneck. Aelmi, Fischer, Dillon, Murphy https://arxiv.org/pdf/1612.00410.pdf https://github.com/alexalemi/vib_demo
Generalized variational inference. Knoblauch, Jewson, Damoulas https://arxiv.org/pdf/1904.02063.pdf
Variational autoencoders and nonlinear ICA: a unifying framework. Khemakhem, Kingma https://arxiv.org/pdf/1907.04809.pdf
Lagging inference networks and posterior collapse in variational autoencoders. He, Spokoyny, Neubig, Berg-Kirkpatrick https://arxiv.org/pdf/1901.05534.pdf https://github.com/jxhe/vae-lagging-encoder
Avoiding latent variable collapse with generative skip models. Dieng, Kim, Rush, Blei https://arxiv.org/pdf/1807.04863.pdf
Distribution Matching in Variational inference. Rosca, Lakshminarayana, Mohamed https://arxiv.org/pdf/1802.06847.pdf A variational auto-encoder model for stochastic point process. Mehrasa, Jyothi, Durand, He, Sigal, Mori https://arxiv.org/pdf/1904.03273.pdf
Sliced-Wasserstein auto-encoders. Kolouri, Pope, Martin, Rohde https://openreview.net/pdf?id=H1xaJn05FQ https://github.com/skolouri/swae
A deep generative model for graph layout. Kwon, Ma https://arxiv.org/pdf/1904.12225.pdf
Differentiable perturb-and-parse semi-supervised parsing with a structured variational autoencoder. Corro, Titov https://openreview.net/pdf?id=BJlgNh0qKQ https://github.com/FilippoC/diffdp
Variational autoencoders with jointly optimized latent dependency structure. He, Gong, Marino, Mori, Lehrmann https://openreview.net/pdf?id=SJgsCjCqt7 https://github.com/ys1998/vae-latent-structure
Unsupervised learning of spatiotemporally coherent metrics Goroshin, Bruna, Tompson, Eigen, LeCun https://arxiv.org/pdf/1412.6056.pdf
Temporal difference variational auto-encoder. Gregor, Papamakarios, Besse, Buesing, Weber https://arxiv.org/pdf/1806.03107.pdf https://github.com/xqding/TD-VAE
Representation learning with contrastive predictive coding. van den Oord, Li, Vinyals https://arxiv.org/pdf/1807.03748.pdf https://github.com/davidtellez/contrastive-predictive-coding
Representation disentanglement for multi-task learning with application to fetal ultrasound Meng, Pawlowski, Rueckert, Kainz https://arxiv.org/pdf/1908.07885.pdf
M$2$VAE - derivation of a multi-modal variational autoencoder objective from the marginal joint log-likelihood. Korthals https://arxiv.org/pdf/1903.07303.pdf
Predicting visual memory schemas with variational autoencoders. Kyle-Davidson, Bors, Evans https://arxiv.org/pdf/1907.08514.pdf
T-CVAE: Transformer -based conditioned variational autoencoder for story completion. Wang, Wan https://www.ijcai.org/proceedings/2019/0727.pdf https://github.com/sodawater/T-CVAE
PuVAE: A variational autoencoder to purify adversarial examples. Hwang, Park, Jang, Yoon, Cho https://arxiv.org/pdf/1903.00585.pdf
Coupled VAE: Improved accuracy and robustness of a variational autoencoder. Cao, Li, Nelson https://arxiv.org/pdf/1906.00536.pdf
D-VAE: A variational autoencoder for directed acyclic graphs. Zhang, Jiang, Cui, Garnett, Chen https://arxiv.org/abs/1904.11088 https://github.com/muhanzhang/D-VAE
Are disentangled representations helpful for abstract reasoning? van Steenkiste, Locatello, Schmidhuber, Bachem https://arxiv.org/pdf/1905.12506.pdf
A heuristic for unsupervised model selection for variational disentangled representation learning. Duan, Watters, Matthey, Burgess, Lerchner, Higgins https://arxiv.org/pdf/1905.12614.pdf
Dual space learning with variational autoencoders. Okamoto, Suzuki, Higuchi, Ohsawa, Matsuo https://pdfs.semanticscholar.org/ea70/6495d4a6214b3d6174bb7fd99c5a9c34c2e6.pdf
Variational autoencoders for sparse and overdispersed discrete data. Zhao, Rai, Du, Buntine https://arxiv.org/pdf/1905.00616.pdf
Variational auto-decoder. Zadeh, Lim, Liang, Morency. https://arxiv.org/pdf/1903.00840.pdf
Causal discovery with attention-based convolutional neural networks. Naura, Bucur, Seifert https://www.mdpi.com/2504-4990/1/1/19/pdf
Bias and generalization in deep generative models: an empirical study. Zhao, Ren, Yuan, Song, Goodman, Ermon https://arxiv.org/pdf/1811.03259.pdf https://ermongroup.github.io/blog/bias-and-generalization-dgm/ https://github.com/ermongroup/BiasAndGeneralization/tree/master/Evaluate
On variational lower bounds of mutual information. Poole, Ozair, van den Oord, Alemi, Tucker http://bayesiandeeplearning.org/2018/papers/136.pdf
GAN - why it is so hard to train generative adversarial networks . Hui https://medium.com/@jonathan_hui/gan-why-it-is-so-hard-to-train-generative-advisory-networks-819a86b3750b
Counterfactuals uncover the modular structure of deep generative models. Besserve, Sun, Scholkopf. https://arxiv.org/pdf/1812.03253.pdf
Learning independent causal mechanisms. Parascandolo, Kilbertus, Rojas-Carulla, Scholkopf https://arxiv.org/pdf/1712.00961.pdf
Emergence of invariance and disentanglement in deep representations. Achille, Soatto https://arxiv.org/pdf/1706.01350.pdf
Variational memory encoder-decoder. Le, Tran, Nguyen, Venkatesh https://arxiv.org/pdf/1807.09950.pdf https://github.com/thaihungle/VMED
Variational autoencoders for collaborative filtering. Liang, Krishnan, Hoffman, Jebara https://arxiv.org/pdf/1802.05814.pdf
Invariant representations without adversarial training. Moyer, Gao, Brekelmans, Steeg, Galstyan http://papers.nips.cc/paper/8122-invariant-representations-without-adversarial-training.pdf https://github.com/dcmoyer/inv-rep
Density estimation: Variational autoencoders. Rui Shu http://ruishu.io/2018/03/14/vae/
TherML: The thermodynamics of machine learning. Alemi, Fishcer https://arxiv.org/pdf/1807.04162.pdf
Leveraging the exact likelihood of deep latent variable models. Mattei, Frellsen https://arxiv.org/pdf/1802.04826.pdf
What is wrong with VAEs? Kosiorek http://akosiorek.github.io/ml/2018/03/14/what_is_wrong_with_vaes.html
Stochastic variational video prediction. Babaeizadeh, Finn, Erhan, Campbell, Levine https://arxiv.org/pdf/1710.11252.pdf https://github.com/alexlee-gk/video_prediction
Variational attention for sequence-to-sequence models. Bahuleyan, Mou, Vechtomova, Poupart https://arxiv.org/pdf/1712.08207.pdf https://github.com/variational-attention/tf-var-attention
FactorVAE Disentangling by factorizing. Kim, Minh https://arxiv.org/pdf/1802.05983.pdf
Disentangling factors of variation with cycle-consistent variational autoencoders. Jha, Anand, Singh, Veeravasarapu https://arxiv.org/pdf/1804.10469.pdf https://github.com/ananyahjha93/cycle-consistent-vae
Isolating sources of disentanglement in VAEs. Chen, Li, Grosse, Duvenaud https://arxiv.org/pdf/1802.04942.pdf
VAE with a VampPrior. Tomczak, Welling https://arxiv.org/pdf/1705.07120.pdf
A Framework for the quantitative evaluation of disentangled representations. Eastwood, Williams https://openreview.net/pdf?id=By-7dz-AZ https://github.com/cianeastwood/qedr
Recent advances in autoencoder based representation learning. Tschannen, Bachem, Lucic https://arxiv.org/pdf/1812.05069.pdf
InfoVAE: Balancing learning and inference in variational autoencoders. Zhao, Song, Ermon https://arxiv.org/pdf/1706.02262.pdf
Understanding disentangling in Beta-VAE. Burgess, Higgins, Pal, Matthey, Watters, Desjardins, Lerchner https://arxiv.org/pdf/1804.03599.pdf
Hidden Talents of the Variational autoencoder. Dai, Wang, Aston, Hua, Wipf https://arxiv.org/pdf/1706.05148.pdf
Variational Inference of disentangled latent concepts from unlabeled observations. Kumar, Sattigeri, Balakrishnan https://arxiv.org/abs/1711.00848
Self-supervised learning of a facial attribute embedding from video. Wiles, Koepke, Zisserman http://www.robots.ox.ac.uk/~vgg/publications/2018/Wiles18a/wiles18a.pdf
Wasserstein auto-encoders. Tolstikhin, Bousquet, Gelly, Scholkopf https://arxiv.org/pdf/1711.01558.pdf
A two-step disentanglement. method Hadad, Wolf, Shahar http://openaccess.thecvf.com/content_cvpr_2018/papers/Hadad_A_Two-Step_Disentanglement_CVPR_2018_paper.pdf https://github.com/naamahadad/A-Two-Step-Disentanglement-Method
Taming VAEs. Rezende, Viola https://arxiv.org/pdf/1810.00597.pdf https://github.com/denproc/Taming-VAEs https://github.com/syncrostone/Taming-VAEs
IntroVAE Introspective variational autoencoders for photographic image synthesis. Huang, Li, He, Sun, Tan https://arxiv.org/pdf/1807.06358.pdf https://github.com/dragen1860/IntroVAE-Pytorch
Information constraints on auto-encoding variational bayes. Lopez, Regier, Jordan, Yosef https://papers.nips.cc/paper/7850-information-constraints-on-auto-encoding-variational-bayes.pdf https://github.com/romain-lopez/HCV
Learning disentangled joint continuous and discrete representations. Dupont https://papers.nips.cc/paper/7351-learning-disentangled-joint-continuous-and-discrete-representations.pdf https://github.com/Schlumberger/joint-vae
Neural discrete representation learning. van den Oord, Vinyals, Kavukcuoglu https://arxiv.org/pdf/1711.00937.pdf https://github.com/1Konny/VQ-VAE https://github.com/ritheshkumar95/pytorch-vqvae
Disentangled sequential autoencoder. Li, Mandt https://arxiv.org/abs/1803.02991 https://github.com/yatindandi/Disentangled-Sequential-Autoencoder
Variational Inference: A review for statisticians. Blei, Kucukelbir, McAuliffe https://arxiv.org/pdf/1601.00670.pdf Advances in Variational Inferece. Zhang, Kjellstrom https://arxiv.org/pdf/1711.05597.pdf
Auto-encoding total correlation explanation. Goa, Brekelmans, Steeg, Galstyan https://arxiv.org/abs/1802.05822 Closest: https://github.com/gregversteeg/CorEx
Fixing a broken ELBO. Alemi, Poole, Fischer, Dillon, Saurous, Murphy https://arxiv.org/pdf/1711.00464.pdf
The information autoencoding family: a lagrangian perspective on latent variable generative models. Zhao, Song, Ermon https://arxiv.org/pdf/1806.06514.pdf https://github.com/ermongroup/lagvae
Debiasing evidence approximations: on importance-weighted autoencoders and jackknife variational inference. Nowozin https://openreview.net/pdf?id=HyZoi-WRb https://github.com/microsoft/jackknife-variational-inference
Unsupervised discrete sentence representation learning for interpretable neural dialog generation. Zhao, Lee, Eskenazi https://vimeo.com/285802293 https://arxiv.org/pdf/1804.08069.pdf https://github.com/snakeztc/NeuralDialog-LAED
Dual swap disentangling. Feng, Wang, Ke, Zeng, Tao, Song https://papers.nips.cc/paper/7830-dual-swap-disentangling.pdf
Multimodal generative models for scalable weakly-supervised learning. Wu, Goodman https://papers.nips.cc/paper/7801-multimodal-generative-models-for-scalable-weakly-supervised-learning.pdf https://github.com/mhw32/multimodal-vae-public https://github.com/panpan2/Multimodal-Variational-Autoencoder
Do deep generative models know what they don't know? Nalisnick, Matsukawa, The, Gorur, Lakshminarayanan https://arxiv.org/pdf/1810.09136.pdf
Glow: generative flow with invertible 1x1 convolutions. Kingma, Dhariwal https://arxiv.org/pdf/1807.03039.pdf https://github.com/openai/glow https://github.com/pytorch/glow
Inference suboptimality in variational autoencoders. Cremer, Li, Duvenaud https://arxiv.org/pdf/1801.03558.pdf https://github.com/chriscremer/Inference-Suboptimality
Adversarial Variational Bayes: unifying variational autoencoders and generative adversarial networks. Mescheder, Mowozin, Geiger https://arxiv.org/pdf/1701.04722.pdf https://github.com/LMescheder/AdversarialVariationalBayes
Semi-amortized variational autoencoders. Kim, Wiseman, Miller, Sontag, Rush https://arxiv.org/pdf/1802.02550.pdf https://github.com/harvardnlp/sa-vae
Spherical Latent Spaces for stable variational autoencoders. Xu, Durrett https://arxiv.org/pdf/1808.10805.pdf https://github.com/jiacheng-xu/vmf_vae_nlp
Hyperspherical variational auto-encoders. Davidson, Falorsi, De Cao, Kipf, Tomczak https://arxiv.org/pdf/1804.00891.pdf https://github.com/nicola-decao/s-vae-tf https://github.com/nicola-decao/s-vae-pytorch
Fader networks: manipulating images by sliding attributes. Lample, Zeghidour, Usunier, Bordes, Denoyer, Ranzato https://arxiv.org/pdf/1706.00409.pdf https://github.com/facebookresearch/FaderNetworks
Training VAEs under structured residuals. Dorta, Vicente, Agapito, Campbell, Prince, Simpson https://arxiv.org/pdf/1804.01050.pdf https://github.com/Garoe/tf_mvg
oi-VAE: output interpretable VAEs for nonlinear group factor analysis. Ainsworth, Foti, Lee, Fox https://arxiv.org/pdf/1802.06765.pdf https://github.com/samuela/oi-vae
infoCatVAE: representation learning with categorical variational autoencoders. Lelarge, Pineau https://arxiv.org/pdf/1806.08240.pdf https://github.com/edouardpineau/infoCatVAE
Iterative Amortized inference. Marino, Yue, Mandt https://arxiv.org/pdf/1807.09356.pdf https://vimeo.com/287766880 https://github.com/joelouismarino/iterative_inference
On unifying Deep Generative Models. Hu, Yang, Salakhutdinov, Xing https://arxiv.org/pdf/1706.00550.pdf
Diverse Image-to-image translation via disentangled representations. Lee, Tseng, Huang, Singh, Yang https://arxiv.org/pdf/1808.00948.pdf https://github.com/HsinYingLee/DRIT
PIONEER networks: progressively growing generative autoencoder. Heljakka, Solin, Kannala https://arxiv.org/pdf/1807.03026.pdf https://github.com/AaltoVision/pioneer
Towards a definition of disentangled representations. Higgins, Amos, Pfau, Racaniere, Matthey, Rezende, Lerchner https://arxiv.org/pdf/1812.02230.pdf
Life-long disentangled representation learning with cross-domain latent homologies. Achille, Eccles, Matthey, Burgess, Watters, Lerchner, Higgins file:///Users/matthewvowels/Downloads/Life-Long_Disentangled_Representation_Learning_wit.pdf
Learning deep disentangled embeddings with F-statistic loss. Ridgeway, Mozer https://arxiv.org/pdf/1802.05312.pdf https://github.com/kridgeway/f-statistic-loss-nips-2018
Learning latent subspaces in variational autoencoders. Klys, Snell, Zemel https://arxiv.org/pdf/1812.06190.pdf
On the latent space of Wasserstein auto-encoders. Rubenstein, Scholkopf, Tolstikhin. https://arxiv.org/pdf/1802.03761.pdf https://github.com/tolstikhin/wae
Learning disentangled representations with Wasserstein auto-encoders. Rubenstein, Scholkopf, Tolstikhin https://openreview.net/pdf?id=Hy79-UJPM
The mutual autoencoder: controlling information in latent code representations. Phuong, Kushman, Nowozin, Tomioka, Welling https://openreview.net/pdf?id=HkbmWqxCZ https://openreview.net/pdf?id=HkbmWqxCZ http://2017.ds3-datascience-polytechnique.fr/wp-content/uploads/2017/08/DS3_posterID_048.pdf
Auxiliary guided autoregressive variational autoencoders. Lucas, Verkbeek https://openreview.net/pdf?id=HkGcX--0- https://github.com/pclucas14/aux-vae
Interventional robustness of deep latent variable models. Suter, Miladinovic, Bauer, Scholkopf https://pdfs.semanticscholar.org/8028/a56d6f9d2179416d86837b447c6310bd371d.pdf?_ga=2.190184363.1450484303.1564569882-397935340.1548854421
Understanding degeneracies and ambiguities in attribute transfer. Szabo, Hu, Portenier, Zwicker, Facaro http://openaccess.thecvf.com/content_ECCV_2018/papers/Attila_Szabo_Understanding_Degeneracies_and_ECCV_2018_paper.pdf DNA-GAN: learning disentangled representations from multi-attribute images. Xiao, Hong, Ma https://arxiv.org/pdf/1711.05415.pdf https://github.com/Prinsphield/DNA-GAN
Normalizing flows. Kosiorek http://akosiorek.github.io/ml/2018/04/03/norm_flows.html
Hamiltonian variational auto-encoder Caterini, Doucet, Sejdinovic https://arxiv.org/pdf/1805.11328.pdf
Causal generative neural networks. Goudet, Kalainathan, Caillou, Guyon, Lopez-Paz, Sebag. https://arxiv.org/pdf/1711.08936.pdf https://github.com/GoudetOlivier/CGNN
Flow-GAN: Combining maximum likelihood and adversarial learning in generative models. Grover, Dhar, Ermon https://arxiv.org/pdf/1705.08868.pdf https://github.com/ermongroup/flow-gan
Linked causal variational autoencoder for inferring paired spillover effects. Rakesh, Guo, Moraffah, Agarwal, Liu https://arxiv.org/pdf/1808.03333.pdf https://github.com/rguo12/CIKM18-LCVA
Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications. Xu, Chen, Zhao, Li, Bu, Li, Liu, Zhao, Pei, Feng, Chen, Wang, Qiao https://arxiv.org/pdf/1802.03903.pdf
Mutual information neural estimation. Belghazi, Baratin, Rajeswar, Ozair, Bengio, Hjelm. https://arxiv.org/pdf/1801.04062.pdf https://github.com/sungyubkim/MINE-Mutual-Information-Neural-Estimation- https://github.com/mzgubic/MINE
Explorations in homeomorphic variational auto-encoding. Falorsi, de Haan, Davidson, Cao, Weiler, Forre, Cohen. https://arxiv.org/pdf/1807.04689.pdf https://github.com/pimdh/lie-vae
Hierarchical variational memory network for dialogue generation. Chen, Ren, Tang, Zhao, Yin http://delivery.acm.org/10.1145/3190000/3186077/p1653-chen.pdf?ip=86.162.136.199&id=3186077&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1569938843_c07ad21d173fc64a44a22fd6521140cb
Discovering causal signals in images . Lopez-Paz, Nishihara, Chintala, Scholkopf, Bottou https://arxiv.org/pdf/1605.08179.pdf
Autoencoding variational inference for topic models. Srivastava, Sutton https://arxiv.org/pdf/1703.01488.pdf
Hidden Markov model variational autoencoder for acoustic unit discovery. Ebbers, Heymann, Drude, Glarner, Haeb-Umbach, Raj https://www.isca-speech.org/archive/Interspeech_2017/pdfs/1160.PDF
Application of variational autoencoders for aircraft turbomachinery design. Zalger http://cs229.stanford.edu/proj2017/final-reports/5231979.pdf
Semi-supervised learning with variational autoencoders. Keng http://bjlkeng.github.io/posts/semi-supervised-learning-with-variational-autoencoders/
Causal effect inference with deep latent variable models. Louizos, Shalit, Mooij, Sontag, Zemel, Welling https://arxiv.org/pdf/1705.08821.pdf https://github.com/AMLab-Amsterdam/CEVAE
beta-VAE: learning basic visual concepts with a constrained variational framework. Higgins, Matthey, Pal, Burgess, Glorot, Botvinick, Mohamed, Lerchner https://openreview.net/pdf?id=Sy2fzU9gl
Challenges in disentangling independent factors of variation. Szabo, Hu, Portenier, Facaro, Zwicker https://arxiv.org/pdf/1711.02245.pdf https://github.com/ananyahjha93/challenges-in-disentangling
Composing graphical models with neural networks for structured representations and fast inference. Johnson, Duvenaud, Wiltschko, Datta, Adams https://arxiv.org/pdf/1603.06277.pdf
Split-brain autoencoders: unsupervised learning by cross-channel prediction. Zhang, Isola, Efros https://arxiv.org/pdf/1611.09842.pdf
Learning disentangled representations with semi-supervised deep generative models.Siddharth, Paige, van de Meent, Desmaison, Goodman, Kohli, Wood, Torr https://papers.nips.cc/paper/7174-learning-disentangled-representations-with-semi-supervised-deep-generative-models.pdf https://github.com/probtorch/probtorch
Learning hierarchical features from generative models. Zhao, Song, Ermon https://arxiv.org/pdf/1702.08396.pdf https://github.com/ermongroup/Variational-Ladder-Autoencoder
Multi-level variational autoencoder: learning disentangled representations from grouped observations. Bouchacourt, Tomioka, Nowozin https://arxiv.org/pdf/1705.08841.pdf
Neural Face editing with intrinsic image disentangling. Shu, Yumer, Hadap, Sankavalli, Shechtman, Samaras http://openaccess.thecvf.com/content_cvpr_2017/papers/Shu_Neural_Face_Editing_CVPR_2017_paper.pdf https://github.com/zhixinshu/NeuralFaceEditing
Variational Lossy Autoencoder. Chen, Kingma, Salimans, Duan, Dhariwal, Schulman, Sutskever, Abbeel https://arxiv.org/abs/1611.02731 https://github.com/jiamings/tsong.me/blob/master/_posts/reading/2016-11-08-lossy-vae.md
Unsupervised learning of disentangled and interpretable representations from sequential data. Hsu, Zhang, Glass https://papers.nips.cc/paper/6784-unsupervised-learning-of-disentangled-and-interpretable-representations-from-sequential-data.pdf https://github.com/wnhsu/FactorizedHierarchicalVAE https://github.com/wnhsu/ScalableFHVAE
Factorized variational autoencoder for modeling audience reactions to movies. Deng, Navarathna, Carr, Mandt, Yue, Matthews, Mori http://www.yisongyue.com/publications/cvpr2017_fvae.pdf
Learning latent representations for speech generation and transformation. Hsu, Zhang, Glass https://arxiv.org/pdf/1704.04222.pdf https://github.com/wnhsu/SpeechVAE
Unsupervised learning of disentangled representations from video. Denton, Birodkar https://papers.nips.cc/paper/7028-unsupervised-learning-of-disentangled-representations-from-video.pdf https://github.com/ap229997/DRNET
Laplacian pyramid of conditional variational autoencoders. Dorta, Vicente, Agapito, Campbell, Prince, Simpson http://cs.bath.ac.uk/~nc537/papers/cvmp17_LapCVAE.pdf
Neural Photo Editing with Inrospective Adverarial Networks. Brock, Lim, Ritchie, Weston https://arxiv.org/pdf/1609.07093.pdf https://github.com/ajbrock/Neural-Photo-Editor
Discrete Variational Autoencoder. Rolfe https://arxiv.org/pdf/1609.02200.pdf https://github.com/QuadrantAI/dvae
Reinterpreting importance-weighted autoencoders. Cremer, Morris, Duvenaud https://arxiv.org/pdf/1704.02916.pdf https://github.com/FighterLYL/iwae
Density Estimation using realNVP. Dinh, Sohl-Dickstein, Bengio https://arxiv.org/pdf/1605.08803.pdf https://github.com/taesungp/real-nvp https://github.com/chrischute/real-nvp
JADE: Joint autoencoders for disentanglement. Banijamali, Karimi, Wong, Ghosi https://arxiv.org/pdf/1711.09163.pdf Joint Multimodal learning with deep generative models. Suzuki, Nakayama, Matsuo https://openreview.net/pdf?id=BkL7bONFe https://github.com/masa-su/jmvae
Towards a deeper understanding of variational autoencoding models. Zhao, Song, Ermon https://arxiv.org/pdf/1702.08658.pdf https://github.com/ermongroup/Sequential-Variational-Autoencoder
Lagging inference networks and posterior collapse in variational autoencoders. Dilokthanakul, Mediano, Garnelo, Lee, Salimbeni, Arulkumaran, Shanahan https://arxiv.org/pdf/1611.02648.pdf https://github.com/Nat-D/GMVAE https://github.com/psanch21/VAE-GMVAE
On the challenges of learning with inference networks on sparse, high-dimensional data. Krishnan, Liang, Hoffman https://arxiv.org/pdf/1710.06085.pdf https://github.com/rahulk90/vae_sparse
Stick-breaking Variational Autoencoder. https://arxiv.org/pdf/1605.06197.pdf https://github.com/sporsho/hdp-vae
Deep variational canonical correlation analysis. Wang, Yan, Lee, Livescu https://arxiv.org/pdf/1610.03454.pdf https://github.com/edchengg/VCCA_pytorch
Nonparametric variational auto-encoders for hierarchical representation learning. Goyal, Hu, Liang, Wang, Xing https://arxiv.org/pdf/1703.07027.pdf https://github.com/bobchennan/VAE_NBP/blob/master/report.markdown
PixelSNAIL: An improved autoregressive generative model. Chen, Mishra, Rohaninejad, Abbeel https://arxiv.org/pdf/1712.09763.pdf https://github.com/neocxi/pixelsnail-public
Improved Variational Inference with inverse autoregressive flows. Kingma, Salimans, Jozefowicz, Chen, Sutskever, Welling https://arxiv.org/pdf/1606.04934.pdf https://github.com/kefirski/bdir_vae
It takes (only) two: adversarial generator-encoder networks. Ulyanov, Vedaldi, Lempitsky https://arxiv.org/pdf/1704.02304.pdf https://github.com/DmitryUlyanov/AGE
Symmetric Variational Autoencoder and connections to adversarial learning. Chen, Dai, Pu, Li, Su, Carin https://arxiv.org/pdf/1709.01846.pdf
Reconstruction-based disentanglement for pose-invariant face recognition. Peng, Yu, Sohn, Metaxas, Chandraker https://arxiv.org/pdf/1702.03041.pdf https://github.com/zhangjunh/DR-GAN-by-pytorch
Is maximum likelihood useful for representation learning? Huszár https://www.inference.vc/maximum-likelihood-for-representation-learning-2/
Disentangled representation learning GAN for pose-invariant face recognition. Tran, Yin, Liu http://zpascal.net/cvpr2017/Tran_Disentangled_Representation_Learning_CVPR_2017_paper.pdf https://github.com/kayamin/DR-GAN
Improved Variational Autoencoders for text modeling using dilated convolutions. Yang, Hu, Salakhutdinov, Berg-kirkpatrick https://arxiv.org/pdf/1702.08139.pdf
Improving variational auto-encoders using householder flow. Tomczak, Welling https://arxiv.org/pdf/1611.09630.pdf https://github.com/jmtomczak/vae_householder_flow
Sticking the landing: simple, lower-variance gradient estimators for variational inference. Roeder, Wu, Duvenaud. http://proceedings.mlr.press/v97/kingma19a/kingma19a.pdf https://github.com/geoffroeder/iwae
VEEGAN: Reducing mode collapse in GANs using implicit variational learning. Srivastava, Valkov, Russell, Gutmann. https://arxiv.org/pdf/1705.07761.pdf https://github.com/akashgit/VEEGAN
Discovering discrete latent topics with neural variational inference. Miao, Grefenstette, Blunsom https://arxiv.org/pdf/1706.00359.pdf
Variational approaches for auto-encoding generative adversarial networks. Rosca, Lakshminarayana, Warde-Farley, Mohamed https://arxiv.org/pdf/1706.04987.pdf
Deep feature consistent variational autoencoder. Hou, Shen, Sun, Qiu https://arxiv.org/pdf/1610.00291.pdf https://github.com/sbavon/Deep-Feature-Consistent-Variational-AutoEncoder-in-Tensorflow
Neural variational inference for text processing. Miao, Yu, Grefenstette, Blunsom. https://arxiv.org/pdf/1511.06038.pdf
Domain-adversarial training of neural networks. Ganin, Ustinova, Ajakan, Germain, Larochelle, Laviolette, Marchand, Lempitsky https://arxiv.org/pdf/1505.07818.pdf
Tutorial on Variational Autoencoders. Doersch https://arxiv.org/pdf/1606.05908.pdf
How to train deep variational autoencoders and probabilistic ladder networks. Sonderby, Raiko, Maaloe, Sonderby, Winther https://orbit.dtu.dk/files/121765928/1602.02282.pdf
ELBO surgery: yet another way to carve up the variational evidence lower bound. Hoffman, Johnson http://approximateinference.org/accepted/HoffmanJohnson2016.pdf
Variational inference with normalizing flows. Rezende, Mohamed https://arxiv.org/pdf/1505.05770.pdf
The Variational Fair Autoencoder. Louizos, Swersky, Li, Welling, Zemel https://arxiv.org/pdf/1511.00830.pdf https://github.com/dendisuhubdy/vfae
Information dropout: learning optimal representations through noisy computations. Achille, Soatto https://arxiv.org/pdf/1611.01353.pdf
Domain separation networks. Bousmalis, Trigeorgis, Silberman, Krishnan, Erhan https://arxiv.org/pdf/1608.06019.pdf https://github.com/fungtion/DSN https://github.com/farnazj/Domain-Separation-Networks
Disentangling factors of variation in deep representations using adversarial training. Mathieu, Zhao, Sprechmann, Ramesh, LeCunn https://arxiv.org/pdf/1611.03383.pdf https://github.com/ananyahjha93/disentangling-factors-of-variation-using-adversarial-training
Variational autoencoder for semi-supervised text classification. Xu, Sun, Deng, Tan https://arxiv.org/pdf/1603.02514.pdf https://github.com/wead-hsu/ssvae related: https://github.com/isohrab/semi-supervised-text-classification
Learning what and where to draw. Reed, Sohn, Zhang, Lee https://arxiv.org/pdf/1610.02454.pdf
Attribute2Image: Conditional image generation from visual attributes. Yan, Yang, Sohn, Lee https://arxiv.org/pdf/1512.00570.pdf
Variational inference with normalizing flows. Rezende, Mohamed https://arxiv.org/pdf/1505.05770.pdf https://github.com/ex4sperans/variational-inference-with-normalizing-flows
Wild Variational Approximations. Li, Liu http://approximateinference.org/2016/accepted/LiLiu2016.pdf
Importance Weighted Autoencoders. Burda, Grosse, Salakhutdinov https://arxiv.org/pdf/1509.00519.pdf https://github.com/yburda/iwae https://github.com/xqding/Importance_Weighted_Autoencoders https://github.com/abdulfatir/IWAE-tensorflow
Stacked What-Where Auto-encoders. Zhao, Mathieu, Goroshin, LeCunn https://arxiv.org/pdf/1506.02351.pdf https://github.com/yselivonchyk/Tensorflow_WhatWhereAutoencoder
Disentangling nonlinear perceptual embeddings with multi-query triplet networks. Veit, Belongie, Karaletsos https://www.researchgate.net/profile/Andreas_Veit/publication/301837223_Disentangling_Nonlinear_Perceptual_Embeddings_With_Multi-Query_Triplet_Networks/links/57e2997308ae040ae3c2f3a3/Disentangling-Nonlinear-Perceptual-Embeddings-With-Multi-Query-Triplet-Networks.pdf
Ladder Variational Autoencoders. Sonderby, Raiko, Maaloe, Sonderby, Winther https://arxiv.org/pdf/1602.02282.pdf Variational autoencoder for deep learning of images, labels and captions. Pu, Gan Henao, Yuan, Li, Stevens, Carin https://papers.nips.cc/paper/6528-variational-autoencoder-for-deep-learning-of-images-labels-and-captions.pdf
Approximate inference for deep latent Gaussian mixtures. Nalisnick, Hertel, Smyth https://pdfs.semanticscholar.org/f6fe/5e8e25994c188ba6a124462e2cc55f2c5a67.pdf https://github.com/enalisnick/mixture_density_VAEs
Auxiliary Deep Generative Models. Maaloe, Sonderby, Sonderby, Winther https://arxiv.org/pdf/1602.05473.pdf https://github.com/larsmaaloee/auxiliary-deep-generative-models
Variational methods for conditional multimodal deep learning. Pandey, Dukkipati https://arxiv.org/pdf/1603.01801.pdf
PixelVAE: a latent variable model for natural images. Gulrajani, Kumar, Ahmed, Taiga, Visin, Vazquez, Courville https://arxiv.org/pdf/1611.05013.pdf https://github.com/igul222/PixelVAE https://github.com/kundan2510/pixelVAE
Adversarial autoencoders. Makhzani, Shlens, Jaitly, Goodfellow, Frey https://arxiv.org/pdf/1511.05644.pdf https://github.com/conan7882/adversarial-autoencoders
A hierarchical latent variable encoder-decoder model for generating dialogues. Serban, Sordoni, Lowe, Charlin, Pineau, Courville, Bengio http://www.cs.toronto.edu/~lcharlin/papers/vhred_aaai17.pdf
Infinite variational autoencoder for semi-supervised learning. Abbasnejad, Dick https://arxiv.org/pdf/1611.07800.pdf
f-GAN: Training generative neural samplers using variational divergence minimization. Nowozin, Cseke https://arxiv.org/pdf/1606.00709.pdf https://github.com/LynnHo/f-GAN-Tensorflow
DISCO Nets: DISsimilarity Coefficient networks Bouchacourt, Kumar, Nowozin https://arxiv.org/pdf/1606.02556.pdf https://github.com/oval-group/DISCONets
Information dropout: learning optimal representations through noisy computations. Achille, Soatto https://arxiv.org/pdf/1611.01353.pdf
Weakly-supervised disentangling with recurrent transformations for 3D view synthesis. Yang, Reed, Yang, Lee https://arxiv.org/pdf/1601.00706.pdf https://github.com/jimeiyang/deepRotator
Autoencoding beyond pixels using a learned similarity metric. Boesen, Larsen, Sonderby, Larochelle, Winther https://arxiv.org/pdf/1512.09300.pdf https://github.com/andersbll/autoencoding_beyond_pixels
Generating images with perceptual similarity metrics based on deep networks Dosovitskiy, Brox. https://arxiv.org/pdf/1602.02644.pdf https://github.com/shijx12/DeepSim
A note on the evaluation of generative models. Theis, van den Oord, Bethge. https://arxiv.org/pdf/1511.01844.pdf
2016 InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. Chen, Duan, Houthooft, Schulman, Sutskever, Abbeel https://arxiv.org/pdf/1606.03657.pdf https://github.com/openai/InfoGAN
Deep learning and the information bottleneck principle Tishby, Zaslavsky https://arxiv.org/pdf/1503.02406.pdf
Training generative neural networks via Maximum Mean Discrepancy optimization. Dziugaite, Roy, Ghahramani https://arxiv.org/pdf/1505.03906.pdf
NICE: non-linear independent components estimation. Dinh, Krueger, Bengio https://arxiv.org/pdf/1410.8516.pdf
Deep convolutional inverse graphics network. Kulkarni, Whitney, Kohli, Tenenbaum https://arxiv.org/pdf/1503.03167.pdf https://github.com/yselivonchyk/TensorFlow_DCIGN
Learning structured output representation using deep conditional generative models. Sohn, Yan, Lee https://papers.nips.cc/paper/5775-learning-structured-output-representation-using-deep-conditional-generative-models.pdf https://github.com/wsjeon/ConditionalVariationalAutoencoder
Latent variable model with diversity-inducing mutual angular regularization. Xie, Deng, Xing https://arxiv.org/pdf/1512.07336.pdf
DRAW: a recurrent neural network for image generation. Gregor, Danihelka, Graves, Rezende, Wierstra. https://arxiv.org/pdf/1502.04623.pdf https://github.com/ericjang/draw
Variational Inference II. Xing, Zheng, Hu, Deng https://www.cs.cmu.edu/~epxing/Class/10708-15/notes/10708_scribe_lecture13.pdf
Auto-encoding variational Bayes. Kingma, Welling https://arxiv.org/pdf/1312.6114.pdf
Learning to disentangle factors of variation with manifold interaction. Reed, Sohn, Zhang, Lee http://proceedings.mlr.press/v32/reed14.pdf
Semi-supervised learning with deep generative models. Kingma, Rezende, Mohamed, Welling https://papers.nips.cc/paper/5352-semi-supervised-learning-with-deep-generative-models.pdf https://github.com/saemundsson/semisupervised_vae https://github.com/Response777/Semi-supervised-VAE
Stochastic backpropagation and approximate inference in deep generative models. Rezende, Mohamed, Wierstra https://arxiv.org/pdf/1401.4082.pdf https://github.com/ashwindcruz/dgm/tree/master/adgm_mnist
Representation learning: a review and new perspectives. Bengio, Courville, Vincent https://arxiv.org/pdf/1206.5538.pdf
Transforming Auto-encoders. Hinton, Krizhevsky, Wang https://www.cs.toronto.edu/~hinton/absps/transauto6.pdf
Graphical models, exponential families, and variational inference. Wainwright, Jordan et al
The information bottleneck method. Tishby, Pereira, Bialek https://arxiv.org/pdf/physics/0004057.pdf
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent