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 ...
Use the following instructions to launch a short training job on QM9/H. See default.py for the longer configurations that reproduce the results in the paper. It should train at around 21.9 steps/s ...
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