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Main Dependencies torch 1.1.0 torchvision 0.3.0 sklearn 0.20.3 numpy 1.19.5 matplotlib 3.0.3 CUDA 10.1 How to run: You can use following command: python svhnmain.py --batch_size=256 --n_epochs=500 --seed=17 --gamma_p=0.5 --gamma_c=0.2 --k2=5 --gpu=0 --dataset=SVHN python mnistmain.py --batch_size=256 --n_epochs=500 --seed=17 --gamma_p=0.5 --gamma_c=0.2 --k2=5 --gpu=1 --dataset=MNIST python fmnistmain.py --batch_size=256 --n_epochs=500 --seed=17 --gamma_p=0.5 --gamma_c=0.2 --k2=5 --gpu=2 --dataset=F-MNIST option choice: fmnistmain.py for F-MNIST dataset svhnmain.py for SVHN dataset mnistmain.py for MNIST dataset --dataset =[SVHN,F-MNIST,MNIST] --gamma_c can be any value in (0,1) --gamma_p can be any value in (0,1) k1 = [1,2,3,5] normal classes number k2 = [1,2,3,5] pollution classes number
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Code for paper Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks
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