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[D] How to improve classification on top of GAN’s z?

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.

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.