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Generative Adversarial Network


LFW. The input images are center cropped by 128 x 128, then resized to 64 x 64.

GAN Setting in details

See the model


  • Adam with very low learning rate, e.g., 1. * 1e-5; otherwise easily fail to nan.
  • Training G by maximizing log D(G(z)) instead could work well in my case.
  • Generated images still remain blurring, we might need a more descriptive generator, e.g., increasing the number of parameters.
  • Human face is very fine-grained, such that difficult to capture and to generate only uinsg random seed?

Generated Image Examples

Epochs are 1, 50, 100, 200, 300, 400, and 500, respectively.

Epoch=001 Epoch=050 Epoch=100 Epoch=200 Epoch=300 Epoch=400 Epoch=500