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Pytorch implementation of conditional generative adversarial network (cGAN) using DCGAN architecture for generating 32x32 images of MNIST, SVHN, FashionMNIST, and USPS datasets.

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Pytorch-cGAN-conditional-GAN

Pytorch implementation of conditional generative adversarial network (cGAN) using DCGAN architecture for generating 32x32 images of MNIST, SVHN, FashionMNIST, and USPS datasets.

Run commands (also available in scripts.sh):

Dataset Run command
MNIST python python main.py --dataset mnist --epochs 10
Fashion MNIST python main.py --dataset fashionmnist --epochs 50
SVHN python main.py --dataset svhn --epochs 100 --n_channels 3
USPS python main.py --dataset usps --epochs 50

Generated Samples

MNIST

SVHN

FashionMNIST

USPS

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Pytorch implementation of conditional generative adversarial network (cGAN) using DCGAN architecture for generating 32x32 images of MNIST, SVHN, FashionMNIST, and USPS datasets.

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