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
tensorflow==1.2.1
scipy==0.17.1
numpy==1.13.1
tensorbayes==0.3.0
To run the MNIST example with classes A = {0, 1}, B = {0, 2},
python run_acgan.py --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 run_acgan.py --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.