ml-lab Goto Github PK

connoisseur

Provenance Analysis in Paintings

consingan

Official implementation of the paper "Improved Techniques for Training Single-Image GANs" by Tobias Hinz, Matthew Fisher, Oliver Wang, and Stefan Wermter

contentdisentanglement

PyTorch implementation of "Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer"

context-aware-zsr

Official code for paper Context-aware Zero-shot Recognition (https://arxiv.org/abs/1904.09320)

context-encoder

Unsupervised Feature Learning by Image Inpainting

context_encoder_pytorch

PyTorch Implement of Context Encoders: Feature Learning by Inpainting

contextlocnet

ContextLocNet: Context-aware Deep Network Models for Weakly Supervised Localization

continual-learning

PyTorch implementation of various methods for continual learning (XdG, EWC, SI, DGR, LwF, Replay-through-Feedback).

continuous-online-video-classification-blog

Code that accompanies a blog posts on continuous online video classification with TensorFlow, Inception and a Raspberry Pi

contrastive-unpaired-translation

PyTorch Implementation of CUT, Contrastive Learning for Unpaired Image-to-Image Translation, ECCV 2020

contrastivelosses4vrd

Implementation for the CVPR2019 paper "Graphical Contrastive Losses for Scene Graph Generation"

conv-net-research

Finding solutions to the problem of catastrophic forgetting that convolutional neural networks can undergo during online task learning.

convai

convai-bot-1337

Skill-based Conversational Agent for NIPS Conversational Intelligence Challenge 2017

convchain

Bitmap generation from a single example with convolutions and MCMC.

convdiclearntensorfactor

Tensor methods have emerged as a powerful paradigm for consistent learning of many latent variable models such as topic models, independent component analysis and dictionary learning. Model parameters are estimated via CP decomposition of the observed higher order input moments. However, in many domains, additional invariances such as shift invariances exist, enforced via models such as convolutional dictionary learning. In this paper, we develop novel tensor decomposition algorithms for parameter estimation of convolutional models. Our algorithm is based on the popular alternating least squares method, but with efficient projections onto the space of stacked circulant matrices. Our method is embarrassingly parallel and consists of simple operations such as fast Fourier transforms and matrix multiplications. Our algorithm converges to the dictionary much faster and more accurately compared to the alternating minimization over filters and activation maps.

conve

Convolutional 2D Knowledge Graph Embeddings resources

convec

convergent_learning

Code for paper "Convergent Learning: Do different neural networks learn the same representations?"

conversation-tensorflow

TensorFlow implementation of Conversation Models

conversational-qg

Implementation for our ACL 2019 paper: Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling

convexcombinations

convgp

Convolutional Gaussian processes based on GPflow.

convlstm

convnet

convnet-benchmarks

Easy benchmarking of all public open-source implementations of convnets

convnetsent

TensorFlow implementation of convolutional neural network for sentence classification tasks

convolutional-attention

Repository for the code of the "A Convolutional Attention Network for Extreme Summarization of Source Code" paper

convolutional-lstm-in-tensorflow

An implementation of convolutional lstms in tensorflow. The code is written in the same style as the basiclstmcell function in tensorflow

convolutional-neural-fabrics

PyTorch implementation of Convolutinal Neural Fabrics http://arxiv.org/abs/1606.02492