cGANs with Projection Discriminator
We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator.
 The paper proposes a simple modification to conditional GANs, obtaining impressive results on both the quality and diversity of samples on ImageNet dataset. Instead of concatenating the condition vector y to the input image x or hidden layers of the discriminator D as in the literature, the authors propose to project the condition y onto a penultimate feature space V of D (by simply taking an inner product between y and V) . This implementation basically restricts the conditional distribution p(y|x) to be really simple and seems to be posing a good prior leading to great empirical results. + Quality: - Simple method leading to great results on ImageNet! - While the paper admittedly leaves theoretical work for future work, the paper would be much stronger if the authors could perform an ablation study to provide readers with more intuition on why this work. One experiment could be: sticking y to every hidden layer of D before the current projection layer, and removing these y*s increasingly and seeing how performance changes. - Appropriate comparison with existing conditional models: AC-GANs and PPGNs. - Appropriate (extensive) metrics were used (Inception score/accuracy, MS-SSIM, FID) + Clarity: - Should explicitly define p, q, r upfront before Equation 1 (or between Eq1 and Eq2). - PPG should be PPGNs. + Originality: This work proposes a simple method that is original compared existing GANs. + Significance: While the contribution is significant, more experiments providing more intuition into why this projection works so well would make the paper much stronger. Overall, I really enjoy reading this paper and recommend for acceptance!
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
cGANs with Projection Discriminator
We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator.
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>



 The paper proposes a simple modification to conditional GANs, obtaining impressive results on both the quality and diversity of samples on ImageNet dataset. Instead of concatenating the condition vector y to the input image x or hidden layers of the discriminator D as in the literature, the authors propose to project the condition y onto a penultimate feature space V of D (by simply taking an inner product between y and V) . This implementation basically restricts the conditional distribution p(y|x) to be really simple and seems to be posing a good prior leading to great empirical results. + Quality: - Simple method leading to great results on ImageNet! - While the paper admittedly leaves theoretical work for future work, the paper would be much stronger if the authors could perform an ablation study to provide readers with more intuition on why this work. One experiment could be: sticking y to every hidden layer of D before the current projection layer, and removing these y*s increasingly and seeing how performance changes. - Appropriate comparison with existing conditional models: AC-GANs and PPGNs. - Appropriate (extensive) metrics were used (Inception score/accuracy, MS-SSIM, FID) + Clarity: - Should explicitly define p, q, r upfront before Equation 1 (or between Eq1 and Eq2). - PPG should be PPGNs. + Originality: This work proposes a simple method that is original compared existing GANs. + Significance: While the contribution is significant, more experiments providing more intuition into why this projection works so well would make the paper much stronger. Overall, I really enjoy reading this paper and recommend for acceptance!
assistant
 The paper proposes a simple modification to conditional GANs, obtaining impressive results on both the quality and diversity of samples on ImageNet dataset. Instead of concatenating the condition vector y to the input image x or hidden layers of the discriminator D as in the literature, the authors propose to project the condition y onto a penultimate feature space V of D (by simply taking an inner product between y and V) . This implementation basically restricts the conditional distribution p(y|x) to be really simple and seems to be posing a good prior leading to great empirical results. + Quality: - Simple method leading to great results on ImageNet! - While the paper admittedly leaves theoretical work for future work, the paper would be much stronger if the authors could perform an ablation study to provide readers with more intuition on why this work. One experiment could be: sticking y to every hidden layer of D before the current projection layer, and removing these y*s increasingly and seeing how performance changes. - Appropriate comparison with existing conditional models: AC-GANs and PPGNs. - Appropriate (extensive) metrics were used (Inception score/accuracy, MS-SSIM, FID) + Clarity: - Should explicitly define p, q, r upfront before Equation 1 (or between Eq1 and Eq2). - PPG should be PPGNs. + Originality: This work proposes a simple method that is original compared existing GANs. + Significance: While the contribution is significant, more experiments providing more intuition into why this projection works so well would make the paper much stronger. Overall, I really enjoy reading this paper and recommend for acceptance!