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Title: Demystifying MMD GANs

Paper content:
We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs.
As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimization process for both MMD GANs and Wasserstein GANs are unbiased, but learning a discriminator based on samples leads to biased gradients for the generator parameters.
We also discuss the issue of kernel choice for the MMD critic, and characterize the kernel corresponding to the energy distance used for the Cramér GAN critic.
Being an integral probability metric, the MMD benefits from training strategies recently developed for Wasserstein GANs.
In experiments, the MMD GAN is able to employ a smaller critic network than the Wasserstein GAN, resulting in a simpler and faster-training algorithm with matching performance.
We also propose an improved measure of GAN convergence, the Kernel Inception Distance, and show how to use it to dynamically adapt learning rates during GAN training.
Generative Adversarial Networks (GANs; BID10 provide a powerful method for general-purpose generative modeling of datasets.
Given examples from some distribution, a GAN attempts to learn a generator function, which maps from some fixed noise distribution to samples that attempt to mimic a reference or target distribution.
The generator is trained to trick a discriminator, or critic, which tries to distinguish between generated and target samples.
This alternative to standard maximum likelihood approaches for training generative models has brought about a rush of interest over the past several years.
Likelihoods do not necessarily correspond well to sample quality BID13 , and GAN-type objectives focus much more on producing plausible samples, as illustrated particularly directly by Danihelka et al. (2017) .
This class of models has recently led to many impressive examples of image generation (e.g. Huang et al., 2017a; Jin et al., 2017; Zhu et al., 2017) .GANs
are, however, notoriously tricky to train (Salimans et al., 2016) . This
might be understood in terms of the discriminator class. BID10
showed that, when the discriminator is trained to optimality among a rich enough function class, the generator network attempts to minimize the Jensen-Shannon divergence between the generator and target distributions. This
result has been extended to general f -divergences by Nowozin et al. (2016) . According
to BID1 , however, it is likely that both the GAN and reference probability measures are supported on manifolds within a larger space, as occurs for the set of images in the space of possible pixel values. These manifolds
might not intersect at all, or at best might intersect on sets of measure zero. In this case, the
Jensen-Shannon divergence is constant, and the KL and reverse-KL divergences are infinite, meaning that they provide no useful gradient for the generator to follow. This helps to explain
some of the instability of GAN training.The lack of sensitivity to distance, meaning that nearby but non-overlapping regions of high probability mass are not considered similar, is a long-recognized problem for KL divergence-based discrepancy measures (e.g. Gneiting & Raftery, 2007, Section 4.2) . It is natural to address
this problem using Integral Probability Metrics (IPMs; Müller, 1997) : these measure the distance between probability measures via the largest discrepancy in expectation over a class of "well behaved" witness functions. Thus, IPMs are able to signal
proximity in the probability mass of the generator and reference distributions. (Section 2 describes this framework
in more detail.) BID1 proposed to use the Wasserstein
distance between distributions as the discriminator, which is an integral probability metric constructed from the witness class of 1-Lipschitz functions. To implement the Wasserstein critic,
Arjovsky et al. originally proposed weight clipping of the discriminator network, to enforce k-Lipschitz smoothness. Gulrajani et al. (2017) improved on
this result by directly constraining the gradient of the discriminator network at points between the generator and reference samples. This new Wasserstein GAN implementation
, called WGAN-GP, is more stable and easier to train.A second integral probability metric used in GAN variants is the maximum mean discrepancy (MMD), for which the witness function class is a unit ball in a reproducing kernel Hilbert space (RKHS). Generative adversarial models based on
minimizing the MMD were first considered by Li et al. (2015) and Dziugaite et al. (2015) . These works optimized a generator to minimize
the MMD with a fixed kernel, either using a generic kernel on image pixels or by modeling autoencoder representations instead of images directly. BID9 instead minimized the statistical power
of an MMD-based test with a fixed kernel. Such approaches struggle with complex natural
images, where pixel distances are of little value, and fixed representations can easily be tricked, as in the adversarial examples of BID10 .Adversarial training of the MMD loss is thus an
obvious choice to advance these methods. Here the kernel MMD is defined on the output of
a convolutional network, which is trained adversarially. Recent notable work has made use of the IPM representation
of the MMD to employ the same witness function regularization strategies as BID1 and Gulrajani et al. (2017) , effectively corresponding to an additional constraint on the MMD function class. Without such constraints, the convolutional features are unstable
and difficult to train BID9 . Li et al. (2017b) essentially used the weight clipping strategy of
Arjovsky et al., with additional constraints to encourage the kernel distribution embeddings to be injective. 1 In light of the observations by Gulrajani et al., however, we use
a gradient constraint on the MMD witness function in the present work (see Sections 2.1 and 2.2).2 Bellemare et al. (2017) 's method, the Cramér GAN, also used the gradient
constraint strategy of Gulrajani et al. in their discriminator network. As we discuss in Section 2.3, the Cramér GAN discriminator is related to
the energy distance, which is an instance of the MMD (Sejdinovic et al., 2013) , and which can therefore use a gradient constraint on the witness function. Note, however, that there are important differences between the Cramér GAN
critic and the energy distance, which make it more akin to the optimization of a scoring rule: we provide further details in Appendix A. Weight clipping and gradient constraints are not the only approaches possible: variance features (Mroueh et al., 2017) and constraints (Mroueh & Sercu, 2017) can work, as can other optimization strategies (Berthelot et al., 2017; Li et al., 2017a) .Given that both the Wasserstein distance and the MMD are integral probability
metrics, it is of interest to consider how they differ when used in GAN training. Bellemare et al. (2017) showed that optimizing the empirical Wasserstein distance
can lead to biased gradients for the generator, and gave an explicit example where optimizing with these biased gradients leads the optimizer to incorrect parameter values, even in expectation. They then claim that the energy distance does not suffer from these problems. As
our main theoretical contribution, we substantially clarify the bias situation
in Section 3. First, we show (Theorem 1) that the natural maximum mean discrepancy estimator, including
the estimator of energy distance, has unbiased gradients when used "on top" of a fixed deep network representation. The generator gradients obtained from a trained representation, however, will be biased relative
to the desired gradients of the optimal critic based on infinitely many samples. This situation is exactly analogous to WGANs: the generator's gradients with a fixed critic are
unbiased, but gradients from a learned critic are biased with respect to the supremum over critics.MMD GANs, though, do have some advantages over Wasserstein GANs. Certainly we would not expect the MMD on its own to perform well on raw image data, since these
data lie on a low dimensional manifold embedded in a higher dimensional pixel space. Once the images are mapped through appropriately trained convolutional layers, however, they can
follow a much simpler distribution with broader support across the mapped domain: a phenomenon also observed in autoencoders (Bengio et al., 2013) . In this setting, the MMD with characteristic kernels BID4 shows strong discriminative performance
between distributions. To achieve comparable performance, a WGAN without the advantage of a kernel on the transformed space
requires many more convolutional filters in the critic. In our experiments (Section 5), we find that MMD GANs achieve the same generator performance as WGAN-GPs
with smaller discriminator networks, resulting in GANs with fewer parameters and computationally faster training. Thus, the MMD GAN discriminator can be understood as a hybrid model that plays to the strengths of both
the initial convolutional mappings and the kernel layer that sits on top.

assistant: text: Explain bias situation with MMD GANs; MMD GANs work with smaller critic networks than WGAN-GPs; new GAN evaluation metric.