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# NIPS 2016 Tutorial: Generative Adversarial Networks.

arXiv: Learning, (2017)

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Abstract

This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs). The tutorial describes: (1) Why generative modeling is a topic worth studying, (2) how generative models work, and how GANs compare to other generative models, (3) the details of how GANs work, (4) research frontiers in GANs...More

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Introduction

- This report1 summarizes the content of the NIPS 2016 tutorial on generative adversarial networks (GANs) (Goodfellow et al, 2014b).
- The tutorial was designed primarily to ensure that it answered most of the questions asked by audience members ahead of time, in order to make sure that the tutorial would be as useful as possible to the audience
- This tutorial is not intended to be a comprehensive review of the field of GANs; many excellent papers are not described here, because they were not relevant to answering the most frequent questions, and because the tutorial was delivered as a two hour oral presentation and did not have unlimited time cover all subjects.
- The slides for the tutorial are available in PDF and Keynote format at the following URLs: http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf

Highlights

- This report1 summarizes the content of the NIPS 2016 tutorial on generative adversarial networks (GANs) (Goodfellow et al, 2014b)
- The tutorial describes: (1) Why generative modeling is a topic worth studying, (2) how generative models work, and how generative adversarial networks compare to other generative models, (3) the details of how generative adversarial networks work, (4) research frontiers in generative adversarial networks, and (5) state-of-the-art image models that combine generative adversarial networks with other methods
- 5.2 Evaluation of generative models. Another highly important research area related to generative adversarial networks is that it is not clear how to quantitatively evaluate generative models
- generative adversarial networks are somewhat harder to evaluate than other generative models because it can be difficult to estimate the likelihood for generative adversarial networks (but it is possible—see Wu et al (2016))
- The only real requirement imposed on the design of the generator by the generative adversarial networks framework is that the generator must be differentiable
- generative adversarial networks can use this supervised ratio estimation technique to approximate many cost functions, including the KL divergence used for maximum likelihood estimation

Results

**Evaluation of generative models**

Another highly important research area related to GANs is that it is not clear how to quantitatively evaluate generative models.- The only real requirement imposed on the design of the generator by the GAN framework is that the generator must be differentiable
- This means that the generator cannot produce discrete data, such as one-hot word or character representations.
- Removing this limitation is an important research direction that could unlock the potential of GANs for NLP.
- Using the REINFORCE algorithm (Williams, 1992)

Conclusion

- GANs are generative models that use supervised learning to approximate an intractable cost function, much as Boltzmann machines use Markov chains to approximate their cost and VAEs use the variational lower bound to approximate their cost.
- GANs can use this supervised ratio estimation technique to approximate many cost functions, including the KL divergence used for maximum likelihood estimation.
- Researchers should strive to develop better theoretical understanding and better training algorithms for this scenario.
- Success on this front would improve many other applications, besides GANs

Funding

- Summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks
- The tutorial describes: Why generative modeling is a topic worth studying, how generative models work, and how GANs compare to other generative models, the details of how GANs work, research frontiers in GANs, and state-of-the-art image models that combine GANs with other methods
- The tutorial was designed primarily to ensure that it answered most of the questions asked by audience members ahead of time, in order to make sure that the tutorial would be as useful as possible to the audience
- This tutorial is not intended to be a comprehensive review of the field of GANs; many excellent papers are not described here, because they were not relevant to answering the most frequent questions, and because the tutorial was delivered as a two hour oral presentation and did not have unlimited time cover all subjects
- The term refers to any model that takes a training set, consisting of samples drawn from a distribution pdata, and learns to represent an estimate of that distribution somehow

Study subjects and analysis

minibatches of sixteen samples: 2

Goodfellow (2014) demonstrated the following connections between minimax GANs, noise-contrastive estimation, and maximum likelihood: all three can be interpreted as strategies for playing a minimax game with the same value function. The biggest difference is in where pmodel lies. For GANs, the generator is pmodel, while for NCE and MLE, pmodel is part of the discriminator. Beyond this, the differences between the methods lie in the update strategy. GANs learn both players with gradient descent. MLE learns the discriminator using gradient descent, but has a heuristic update rule for the generator. Specifically, after each discriminator update step, MLE copies the density model learned inside the discriminator and converts it into a sampler to be used as the generator. NCE never updates the generator; it is just a fixed source of noise. Two minibatches of sixteen samples each, generated by a generator network using batch normalization. These minibatches illustrate a problem that occurs occasionally when using batch normalization: fluctuations in the mean and standard deviation of feature values in a minibatch can have a greater effect than the individual z codes for individual images within the minibatch. This manifests here as one minibatch containing all orange-tinted samples and the other containing all green-tinted samples. The examples within a minibatch should be independent from each other, but in this case, batch normalization has caused them to become correlated with each other. An illustration of the mode collapse problem on a two-dimensional toy dataset. In the top row, we see the target distribution pdata that the model should learn. It is a mixture of Gaussians in a two-dimensional space. In the lower row, we see a series of different distributions learned over time as the GAN is trained. Rather than converging to a distribution containing all of the modes in the training set, the generator only ever produces a single mode at a time, cycling between different modes as the discriminator learns to reject each one. Images from Metz et al (2016)

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