Wasserstein Adversarial Domain Adaptation Model for Image Recognition
In this respository, we implmented our proposed Wasserstein adversarial domain adaptation (WADA) model for object recognition. Download link for dataset used to evaluate the model can be find in "data description" section.
Our WADA model consists of source extractor, target extractor, task classifier, feature relator, and domain discriminator. Since optimal transport based metric like Wasserstein distance can avoid gradient vanishing caused in
Data Description
- Digit dataset
- MNIST
- MNISTM
- USPS
- Office dataset
- Amazon
- DSLR
- Webcam
Experimental Settings
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Framework
- Pytorch 0.4.1
-
Hardware
- CPU: Intel Core i7-7700 @3.60 GHz
- RAM: DDR3 2400MHz 64GB
- GPU: Geforce GTX 1080Ti 11GB
- CUDA Version: 9.0.176
- CuDNN Version: 7.1.1
Experimental Results
Data distribution before adaptation trainning
Here blue region represents source domain and red regrion represents target domain
From left to right are t-SNE embedding plot of MNISTM, USPS, and Office dataset rspectively
Following sections are the results of our model under partial and non-partial setting
To better visualize adaptation performance, we color the t-SNE plot according to domain and class
- left hand side: colored by data domain
- right hand side: colored by data class