InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel (UC Berkeley, Open AI) Presenter: Shuhei M. Yoshida (Dept. of Physics, UTokyo) Unsupervised learning of disentangled representations Goal GANs + Maximizing Mutual Information between generated images and input codes Approach Benefit nterpretable representation obtained ithout supervision and substantial additional costs Reference https://arxiv.org/abs/1606.03657 (with Appendix sections) Implementations https://github.com/openai/InfoGAN (by the authors, with TensorFl https://github.com/yoshum/InfoGAN (by the presenter, with Chaine NIPS2016 読読読
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial
NetsXi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel (UC Berkeley, Open AI)
Presenter: Shuhei M. Yoshida (Dept. of Physics, UTokyo)
Unsupervised learning of disentangled representationsGoal
GANs + Maximizing Mutual Information between generated images and input codes
Approach
BenefitInterpretable representation obtainedwithout supervision and substantial additional costs
Reference https://arxiv.org/abs/1606.03657 (with Appendix sections)Implementationshttps://github.com/openai/InfoGAN (by the authors, with TensorFlow)https://github.com/yoshum/InfoGAN (by the presenter, with Chainer)
NIPS2016 読み会
MotivationHow can we achieveunsupervised learning of disentangled representation?
In general, learned representation is entangled, i.e. encoded in a data space in a complicated manner
When a representation is disentangled, it would be more interpretable and easier to apply to tasks
Related works • Unsupervised learning of representation
(no mechanism to force disentanglement)Stacked (often denoising) autoencoder, RBMMany others, including semi-supervised approach
• Weakly supervised learning of disentangled representationdisBM, DC-IGN
• Unsupervised learning of disentangled representationhossRBM, applicable only to discrete latent factors
which the presenter has almost no knowledge about.
This work: Unsupervised learning of disentangled representation applicable to both continuous and discrete latent factors
Generative Adversarial Nets(GANs)Generative model trained by competition between two neural nets:Generator
: an arbitrary noise distributionDiscriminator :
probability that is sampled from the data dist. rather than generated by the generator
whereOptimization problem to solve:
Problems with GANsFrom the perspective of representation learning:No restrictions on how uses • can be used in a highly entangled way• Each dimension of does not represent
any salient feature of the training data
𝑧1
𝑧 2
𝐺 (𝑧 )
𝐺 (𝑧 )
Proposed Resolution: InfoGAN -Maximizing Mutual Information -Observation in conventional GANs:a generated date does not have much information on the noise from which is generatedbecause of heavily entangled use of
Proposed resolution = InfoGAN:the generator trained so that it maximize the mutual information between the latent code and the generated data
min𝐺max𝐷
{𝑉 GAN (𝐺 ,𝐷 )−𝜆 𝐼 (𝐶|𝑋=𝐺 (𝑍 ,𝐶 ) )}
Mutual Information where• :
Entropy of the prior distribution • :
Entropy of the posterior distribution
𝑝 (𝑋=𝑥 )
𝑥
𝑝 (𝑋=𝑥∨𝑌=𝑦 )
𝑥
𝑝 (𝑋=𝑥∨𝑌=𝑦 )
𝑥𝐼 ( 𝑋 ;𝑌 )=0 𝐼 ( 𝑋 ;𝑌 )>0
Sampling
Avoiding increase of calculation costsMajor difficulty: Evaluation of based on evaluation and sampling from the posterior
Two strategies:Variational maximization of mutual information
Use an approximate function Sharing the neural net
between and the discriminator
Variational Maximization of MIFor an arbitrary function ,𝐸𝑥∼𝑝𝐺 ( 𝑋 ) ,𝑐∼𝑝 (𝐶∨𝑋=𝑥 ) [ ln𝑝 (𝐶=𝑐|𝑋=𝑥 ) ]