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Last Commit
Aug. 30, 2018
Nov. 7, 2017


Code to reproduce the experiments in Bayesian Deep Learning workshop paper AC-GAN Learns a Biased Distribution. The following experiments demonstrate that AC-GAN down-samples points near the decision boundary of the auxiliary classifier.


You'll need



To run the MNIST example with classes A = {0, 1}, B = {0, 2},

python --trg mnistbias --info INFO --cw CW

where INFO and CW are values for the mutual information weight and the classification weight.

To run with the real MNIST data set,

python --trg mnist32 --info INFO --cw CW

The defaults are INFO = 1 and CW = 1. Note the following correspondences:

GAN: INFO = 0, CW = 0
InfoGAN: INFO > 0, CW = 0
AC-GAN: INFO > 0, CW > 0


By increasing INFO (denote as lambda_m in the following picture), we can visually observe that 0's get down-sampled.

When we run AC-GAN (with lambda_m = 2) on the real MNIST data, we also get to see something interesting. In the real dataset, 1's sometimes have serifs:

But when we run AC-GAN, it down-samples serifs:

1's with serifs are likely closer to the auxiliary classifier's decision boundary (for the digit classification problem) since serifs make the 1 look closer to a 2. Funnily enough, because AC-GAN down-samples class-ambiguous samples, it actually achieves a better Inception Score than the real MNIST digits.