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Pytorch implementation of Generative Adversarial Networks

于 2022-03-21 发布文件大小:17.66 MB

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# pytorch-MNIST-CelebA-GAN-DCGAN Pytorch implementation of Generative Adversarial Networks (GAN) [1] and Deep Convolutional Generative Adversarial Networks (DCGAN) [2] for MNIST [3] and CelebA [4] datasets. * If you want to train using cropped CelebA dataset, you have to change isCrop = False to isCrop = True. * you can download - MNIST dataset: http://yann.lecun.com/exdb/mnist/ - CelebA dataset: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html * pytorch_CelebA_DCGAN.py requires 64 x 64 size image, so you have to resize CelebA dataset (celebA_data_preprocess.py). * pytorch_CelebA_DCGAN.py added learning rate decay code. ## Implementation details * GAN ![GAN](pytorch_GAN.png) * DCGAN ![Loss](pytorch_DCGAN.png) ## Resutls ### MNIST

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