专栏首页CreateAMindInfoGAN、GAN训练不稳定因素分析

InfoGAN、GAN训练不稳定因素分析

InfoGAN: using the variational bound on mutual information (twice)

Many people have recommended me the infoGAN paper, but I hadn't taken the time to read it until recently. It is actually quite cool:

Summary of this note

  • I show how the original GAN algorithm can be derived using exactly the same variational lower-bound that the authors use in this paper (see also this blog post by Yingzhen)
  • However, GANs use the bound in the wrong direction and end up minimising a lower bound which is not a good thing to do
  • InfoGANs can be expressed purely in terms of mutual information, and applying the variational bound twice: once in the correct direction, once in the wrong direction
  • I believe that the unstable behaviour of GANs is partially explained by using the bound in the incorrect way

Mini-review

The InfoGAN idea is pretty simple. The paper presents an extension to the GAN objective. A new term encourages high mutual information between generated samples and a small subset of latent variables cc. The hope is that by forcing high information content, we cram the most interesting aspects of the representation into cc.

If we were successful, cc ends up representing the most salient and most meaningful sources of variation in the data, while the rest of the noise variables zz will account for additional, meaningless sources of variation and can essentially be dismissed as uncompressible noise.

In order to maximise the mutual information, the authors make use of a variational lower bound. This, conveniently, results in a recognition model, similar to the one we see in variational autoencoders. The recognition model infers latent representation cc from data.

The paper is pretty cool, the results are convincing. I found the notation and derivation a bit confusing, so here is my mini-review:

  • I think the introduction, I don't think it's fair to say "To the best of our knowledge, the only other unsupervised method that learns disentangled representations is hossRBM". There are loads of other methods that attempt this.
  • I believe Lemma 5.1 is basically a trivial application of the theorem of total expectation, and I really don't see the need to provide a proof for that (maybe reviewers asked for a proof).

ref paper eqn 5

My view on InfoGANs

I think there is an interesting connection that the authors did not mention (frankly, it probably would have overcomplicated the presentation). The connection is that original GAN objective itself can be derived from mutual information, and in fact, the discriminator D can be thought of as a variational auxillary variable, exactly the same role as the recognition model q(c|x)q(c|x) in the InfoGAN paper.

The connection relies on the interpretation of Jensen-Shannon divergence as mutual information (see e.g. Yingzen's blog postGANs, mutual information, and possibly algorithm selection?). Here is my graphical model view on InfoGANs that may put things in a slightly different light:

Let's consider the joint distribution of a bunch of varibles:

Now, the main problem is with this derivation is that we were supposed to minimise ℓGANℓGAN, so we really would like an upper bound instead of a lower bound. But the variational method only provides a lower bound. Therefore,

GANs minimise a lower bound, which I believe accounts for some of their unstable behaviour

InfoGANs use the bound twice

Recall that the idealised InfoGAN objective is the weighted difference of two mutual information terms.

To arrive at the algorithm the authors used, one uses the bound on both mutual information terms.

  • When you apply the bound on the first term, you get a lower bound, and you introduce an auxillary distribution that ends up being called the discriminator. This application of the bound is wrong because it bounds the loss function from the wrong side.
  • When you apply the bound on the second term, you end up upper bounding the loss function, because of the negative sign. This is a good thing. The combination of a lower bound and an upper bound means that you don't even know which direction you're bounding or approximating the loss function from anymore, it's neither an upper or a lower bound.

本文由zdx3578推荐。

本文分享自微信公众号 - CreateAMind(createamind),作者:Ferenc Huszár

原文出处及转载信息见文内详细说明,如有侵权,请联系 yunjia_community@tencent.com 删除。

原始发表时间:2016-11-12

本文参与腾讯云自媒体分享计划,欢迎正在阅读的你也加入,一起分享。

我来说两句

0 条评论
登录 后参与评论

相关文章

  • RND 笔记

    RND: https://blog.openai.com/reinforcement-learning-with-prediction-based-reward...

    用户1908973
  • AI尝试做判断题和填空题的效果

    code: https://github.com/createamind/keras-cpcgan

    用户1908973
  • Time-Contrastive Learning for Latent Variable Models

    "Aapo did it again!" - I exclaimed while reading this paper yesterday on the tra...

    用户1908973
  • RND 笔记

    RND: https://blog.openai.com/reinforcement-learning-with-prediction-based-reward...

    用户1908973
  • RFC2616-HTTP1.1-Header Field Definitions(头字段规定部分—单词注释版)

    zaking
  • C. NEKO's Maze Game

    time limit per test:1.5 seconds memory limit per test:256 megabytes inputstandar...

    某些人
  • linux kernel Documentation filesystems overlayfs

    Please see MAINTAINERS file for where to send questions.

    heidsoft
  • Create a natural language classifier that identifies spam

    With the advent of cognitive computing and smart machines, machine learning and ...

    首席架构师智库
  • Palabos Tutorial 2/3:Understanding the multi-block structure

    The code structure of Palabos programs is driven by the duality between atomic-b...

    周星星9527
  • QTX潮玩展 | 艺展美陈空间设计揭秘

    ? 前言 Foreword 在上篇QQ潮玩展的文章里给大家分享了有关「创意品牌设计系统」的构思过程,想必大家都意犹未尽。好的展会品牌系统也要有极具创意的美陈装...

    腾讯ISUX

扫码关注云+社区

领取腾讯云代金券