Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Update: Author rebuttal promised to update the paper to address my criticism regarding clarity of methodological explanation. Contingent upon these changes to the camera ready, I have decided to increase my score from a 7 to an 8. This paper provides a view of generative adversarial networks through the lens of energy-based models (EBM). A slight modification to the original GAN objective falls out of this view, which provides a number of beneficial properties. Overall, I think this is a strong paper. It addresses numerous deficiencies in current GAN training methodology, namely the difficulty with evaluating the results and monitoring the stability of the training. The fact that the representations it learns are SOTA for unsupervised approaches on CIFAR10 is also impressive My main complaint is that, after reading, it is not entirely clear to me to what extent this strategy deviates from typical GAN training. Phrases like “we examine GANs through the lens of … “ (abstract) and “GANs can be reinterpeted” (conclusion) are used which seem to imply that the proposed approach does not alter ordinary GAN algorithms and is just a different perspective. However, the proposed approach *does* in fact alter the algorithm (line 72). More confusion is added when Equation 3 is introduced as an “optimization procedure”, when in fact it is merely a value function that does not immediately imply a procedure or algorithm. It would be great if the authors could clearly and concisely state how their method deviates from standard GAN training, as right now this information is scattered throughout the paper. This paper is also weakened a bit by the exclusion of code, the inclusion of which would greatly improve the accessibility of the work.
This paper continues along a thread in the literature linking GANs and deep energy-based models, the basic idea being that the discriminator can represent an energy function for the distribution and the generator a sampler for the same; this allows, among other things, a sampling approximation of the negative phase term (the gradient of the partition function) using samples from the generator. Taking this view, the manuscript under consideration proposes to leverage the gradient of the discriminator’s parameters to produce both Fisher vectors and a (diagonal approximation to the) Fisher information matrix for the model distribution. This allows for a powerful form of unsupervised representation learning, an induced distance metric (both between points and between sets of points, by applying the distance measure to the means of the sets). Overall, I feel this is a solid piece of generative model research. It proposes a fresh take on well-worn territory, makes several principled contributions as regards training methodology, and empirical demonstrate the method’s usefulness, in particular a classification result from unsupervised representation learning that is quite impressive. My criticisms below mainly concern the text. Obviously I’d really like to see how this stacks up on full-resolution ImageNet classification against recently proposed mutual information and BiGAN approaches, but I understand this could be resource-intensive and perhaps not feasible during the rebuttal period. One glaring omission from the discussion of related work is Ravuri et al’s method of learned moments work, which also uses the discriminator parameter gradient albeit for a different purpose. Lines 100-102 start off a sentence with “Intuitively” and then continue with an “if and only if” statement. Surely something more formal can be said, and this “intuition” can be made concrete. Footnote on page 3 refers to entropy regularization but AFAIK the entropy is not computed or approximated in the referenced work, rather “minibatch features” are computed as a way of detecting low diversity. Section 3.2 is a bit fuzzy on the details, and in particular the repeated references to MCMC are confusing. As best I can determine the loss term is MCMC *inspired*, but MCMC is not performed (otherwise lots of details are missing!). I’d try to make this clearer throughout. Also the use of the script I both for the Fisher Information and for the Identity matrix on 135 is potentially very confusing. The loss term proposed is quite similar in spirit to the historical averaging discussed in Salimans et al, except that it is realized in sample space rather than parameter space. It would probably be appropriate to make this connection explicit. 5.1 mentions a sigmoid nonlinearity (omitted from the referenced work since it was a WGAN) but then a least squares loss. Can you confirm, and clarify in the text, that it is a least squares loss applied to sigmoidally squashed outputs? This seems quite strange, though not unheard of (if I recall correctly, contractive autoencoders of Rifai et al used something similar). Probably appropriate to add Deep Infomax to the results table for CIFAR10 classification (to which you are superior). Re: “higher propensity to overfitting”: it’s not obvious to me that this should be the case, given that while the representations are higher dimensional they are also highly structured, as opposed to something like a feedforward autoencoder bottleneck. Post-rebuttal: I've reviewed the authors' responses, and am satisfied with their clarifications. While larger scale experiments would be ideal, I note and understand that there are difficulties inherent. I believe with the modifications and clarifications requested this is a very solid contribution, and have raised my score to an 8.
Thanks for the rebuttal. I have read it carefully, I stand by my original review and rating. The additional experimental results look good, and except for the scalability issue, most of my concerns are addressed. -------------------------------------------------------- The contributions are interesting and the paper is well written, the authors conduct extensive experiments on several tasks, including unsupervised feature extraction on CIFAR10 and CIFAR100, evaluated by a linear classifier, both the quantitative classification accuracy and the qualitative distance matrix demonstrates the AFV is an effective feature extractor despite at the cost of increased dimensionality of feature space. The paper also presents experimental results on using fisher distance to check the training process. It shows Fisher distance is a good substitute for objective metrics IS and FID when there is no classifier. Some detailed comments are listed below. - I would suggest adding some literature of fisher information and its use case in representation learning such as . - Formula 4 and 5, why use samples from generator distribution to estimate the fisher score/information rather than real samples or combined? - One concern is the scalability issue, even with diagonalization the resulting fisher vector is still proportional to the number of parameters. In large scale GANs, such as , the method could be intractable. - It’s useful if the authors could list the performance of supervised training with the same architecture on CIFAR for comparison in table 1. - what type of gan loss function did the authors use in section 5.3? Besides varying the number of training examples, I think the authors should try different type of gans and check whether fisher distance is able to monitor the training dynamics for miss modes or gradient saturation.  Achille, Alessandro, et al. "Task2Vec: Task Embedding for Meta-Learning." arXiv preprint arXiv:1902.03545 (2019).  Brock, A., Donahue, J., & Simonyan, K. (2018). Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096.