Hello all,

i got a simple question about supervised learning using hidden feature from deep-generative model.

Assume that we have to generate a MNIST while also classifying it, then VAE might be an go-to option because it solves MLE problem. Reducing mode-covering metric=KL(P_real || P_fake); Bleeds probability mass to wherever data is, makes easy to discriminate a feature although it generates blurred images.

But GAN solves minimax problem; It may be fallen in mode-collapse, or capture more sharp-density distribution than VAE. I think it may harm the performance of classification. My question may be boiled-down into ‘How to make GAN’s hidden feature more interpretable?’

– Is it true that GAN suffers from classification problem?

– Then is there any good-idea about solving classification combined with GAN?

– Maybe preparing more complex classifier is an option to matching highly non-linear feature manifold into class labels.

– I hardly found out some breaking-through papers dealing with such GAN-classification problems.

Any discussion, advice, recommendation on papers are very appreciated in advance.