Energy-based Generative Adversarial Networks
We introduce the *Energy-based Generative Adversarial Network* model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions. Similar to the probabilistic GANs, a generator is seen as being trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign high energies to these generated samples. Viewing the discriminator as an energy function allows to use a wide variety of architectures and loss functionals in addition to the usual binary classifier with logistic output. Among them, we show one instantiation of EBGAN framework as using an auto-encoder architecture, with the energy being the reconstruction error, in place of the discriminator. We show that this form of EBGAN exhibits more stable behavior than regular GANs during training. We also show that a single-scale architecture can be trained to generate high-resolution images.
This paper introduces an energy-based Generative Adversarial Network (GAN) and provides theoretical and empirical results modeling a number of image datasets (including large-scale versions of categories of ImageNet). As far as I know energy-based GANs (EBGAN) were introduced in Kim and Bengio (2016), but the proposed version makes a number of different design choices. First, it does away with the entropy regularization term that Kim and Bengio (2016) introduced to ensure that the GAN discriminator converged to an energy function proportional to the log density of the data (at optimum). This implies that the discriminator in the proposed scheme will become uniform at convergence as discussed in the theoretical section of the paper, however the introductory text seems to imply otherwise -- that one could recover a meaningful score function from the trained energy-function (discriminator). This should be clarified. Second, this version of the EBGAN setting includes two innovations: (1) the introduction of the hinge loss in the value function, and (2) the use of an auto-encoder parametrization for the energy function. These innovations are not empirically justified in any way - this is disappointing, as it would be really good to see empirical results supporting the arguments made in support of their introduction. The two significant contributions of this paper are the theoretical analysis of the energy-baesd GAN formalism (showing that the optimum corresponds to a Nash equilibrium) and the impressive empirical results on large images that set a new standard in what straight GAN-style models can achieve. The theoretical results seem solid to me and make a nice contribution. Regarding the quantitative results in Table 2, it seems not appropriate to bold the EBGAN line when it seems to be statistically indistinguishable form the Rasmus et al (2015) results. Though it is not mentioned here, the use of bold typically indicates the state-of-the-art. I think this paper could be be much stronger if the two novel contributions to the energy-based GAN setting were more thoroughly explored with ablation experiments. That being said, I think this paper has already become a contribution other are building on (including at least two other ICLR submissions) and so I think it should be accepted for publication at ICLR.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Energy-based Generative Adversarial Networks
We introduce the *Energy-based Generative Adversarial Network* model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions. Similar to the probabilistic GANs, a generator is seen as being trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign high energies to these generated samples. Viewing the discriminator as an energy function allows to use a wide variety of architectures and loss functionals in addition to the usual binary classifier with logistic output. Among them, we show one instantiation of EBGAN framework as using an auto-encoder architecture, with the energy being the reconstruction error, in place of the discriminator. We show that this form of EBGAN exhibits more stable behavior than regular GANs during training. We also show that a single-scale architecture can be trained to generate high-resolution images.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper introduces an energy-based Generative Adversarial Network (GAN) and provides theoretical and empirical results modeling a number of image datasets (including large-scale versions of categories of ImageNet). As far as I know energy-based GANs (EBGAN) were introduced in Kim and Bengio (2016), but the proposed version makes a number of different design choices. First, it does away with the entropy regularization term that Kim and Bengio (2016) introduced to ensure that the GAN discriminator converged to an energy function proportional to the log density of the data (at optimum). This implies that the discriminator in the proposed scheme will become uniform at convergence as discussed in the theoretical section of the paper, however the introductory text seems to imply otherwise -- that one could recover a meaningful score function from the trained energy-function (discriminator). This should be clarified. Second, this version of the EBGAN setting includes two innovations: (1) the introduction of the hinge loss in the value function, and (2) the use of an auto-encoder parametrization for the energy function. These innovations are not empirically justified in any way - this is disappointing, as it would be really good to see empirical results supporting the arguments made in support of their introduction. The two significant contributions of this paper are the theoretical analysis of the energy-baesd GAN formalism (showing that the optimum corresponds to a Nash equilibrium) and the impressive empirical results on large images that set a new standard in what straight GAN-style models can achieve. The theoretical results seem solid to me and make a nice contribution. Regarding the quantitative results in Table 2, it seems not appropriate to bold the EBGAN line when it seems to be statistically indistinguishable form the Rasmus et al (2015) results. Though it is not mentioned here, the use of bold typically indicates the state-of-the-art. I think this paper could be be much stronger if the two novel contributions to the energy-based GAN setting were more thoroughly explored with ablation experiments. That being said, I think this paper has already become a contribution other are building on (including at least two other ICLR submissions) and so I think it should be accepted for publication at ICLR.
assistant
This paper introduces an energy-based Generative Adversarial Network (GAN) and provides theoretical and empirical results modeling a number of image datasets (including large-scale versions of categories of ImageNet). As far as I know energy-based GANs (EBGAN) were introduced in Kim and Bengio (2016), but the proposed version makes a number of different design choices. First, it does away with the entropy regularization term that Kim and Bengio (2016) introduced to ensure that the GAN discriminator converged to an energy function proportional to the log density of the data (at optimum). This implies that the discriminator in the proposed scheme will become uniform at convergence as discussed in the theoretical section of the paper, however the introductory text seems to imply otherwise -- that one could recover a meaningful score function from the trained energy-function (discriminator). This should be clarified. Second, this version of the EBGAN setting includes two innovations: (1) the introduction of the hinge loss in the value function, and (2) the use of an auto-encoder parametrization for the energy function. These innovations are not empirically justified in any way - this is disappointing, as it would be really good to see empirical results supporting the arguments made in support of their introduction. The two significant contributions of this paper are the theoretical analysis of the energy-baesd GAN formalism (showing that the optimum corresponds to a Nash equilibrium) and the impressive empirical results on large images that set a new standard in what straight GAN-style models can achieve. The theoretical results seem solid to me and make a nice contribution. Regarding the quantitative results in Table 2, it seems not appropriate to bold the EBGAN line when it seems to be statistically indistinguishable form the Rasmus et al (2015) results. Though it is not mentioned here, the use of bold typically indicates the state-of-the-art. I think this paper could be be much stronger if the two novel contributions to the energy-based GAN setting were more thoroughly explored with ablation experiments. That being said, I think this paper has already become a contribution other are building on (including at least two other ICLR submissions) and so I think it should be accepted for publication at ICLR.