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Learning deep representations by mutual information estimation and maximization

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Deep InfoMax Pytorch

Pytorch implementation of Deep InfoMax

Encoding data by maximimizing mutual information between the latent space and in this case, CIFAR 10 images.

Ported most of the code from rcallands chainer implementation. Thanks buddy!

Pytorch implementation by the research team here

Current Results (work in progress)

| |airplane |automobile | bird | cat | deer| dog | frog| horse| ship| truck| |-----------------|-------|--------|-------|-------|-------|-------|-------|-------|-------|------| |Fully supervised |0.7780 | 0.8907 | 0.6233| 0.5606| 0.6891| 0.6420| 0.7967| 0.8206| 0.8619| 0.8291 |DeepInfoMax-Local|0.6120 | 0.6969 | 0.4020| 0.4226| 0.4917| 0.5806| 0.6871| 0.5806| 0.6855| 0.5647


Figure 1
Top: a red lamborghini, Middle: 10 closest images in the latent space (L2 distance), Bottom: 10 farthest images in the latent space.

Some more results..





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