Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole, based on the surrounding pixels. The in-painted images are then presented to a discriminator network that judges if they are real (unaltered training images) or not. This task acts as a regularizer for standard supervised training of the discriminator. Using our approach we are able to directly train large VGG-style networks in a semi-supervised fashion. We evaluate on STL-10 and PASCAL datasets, where our approach obtains performance comparable or superior to existing methods.
This paper proposes a method to incorporate super-resolution and inpainting in the GAN framework for semi-supervised learning using the GAN discriminative features on larger images. The core idea of the paper is not very novel. The usefulness of the GAN discriminative features for semi-supervised learning is already established in previous works such as CatGAN, DCGAN and Salimans et al. However this paper does a good job in actually getting the semi-supervised GAN framework working on larger images such as STL-10 and Pascal datasets using the proposed context conditioning approach, and achieves the state-of-the-art on these datasets. I think that the authors should provide the SSL-GAN baseline for the PASCAL dataset as it is very important to compare the contribution of the context conditioning idea with the standard way of using GAN for semi-supervised learning, i.e., SSL-GAN. I can*t see why the SSL-GAN can not be applied to the 64*64 and 96*96 version of the Pascal dataset (Table 3). If they have trouble training the vanilla GAN on Pascal even on the 64*64 image size, this should be mentioned in the paper and be explained. I am concerned about this specially because CC-GAN almost matches the SSL-GAN baseline on STL-10, and CC-GAN2, to me, seems like a hacky way to improve upon the core CC-GAN idea. So it would be great to compare CC-GAN and SSL-GAN on some other dataset, even if it is a downsampled PASCAL dataset.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole, based on the surrounding pixels. The in-painted images are then presented to a discriminator network that judges if they are real (unaltered training images) or not. This task acts as a regularizer for standard supervised training of the discriminator. Using our approach we are able to directly train large VGG-style networks in a semi-supervised fashion. We evaluate on STL-10 and PASCAL datasets, where our approach obtains performance comparable or superior to existing methods.
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 proposes a method to incorporate super-resolution and inpainting in the GAN framework for semi-supervised learning using the GAN discriminative features on larger images. The core idea of the paper is not very novel. The usefulness of the GAN discriminative features for semi-supervised learning is already established in previous works such as CatGAN, DCGAN and Salimans et al. However this paper does a good job in actually getting the semi-supervised GAN framework working on larger images such as STL-10 and Pascal datasets using the proposed context conditioning approach, and achieves the state-of-the-art on these datasets. I think that the authors should provide the SSL-GAN baseline for the PASCAL dataset as it is very important to compare the contribution of the context conditioning idea with the standard way of using GAN for semi-supervised learning, i.e., SSL-GAN. I can*t see why the SSL-GAN can not be applied to the 64*64 and 96*96 version of the Pascal dataset (Table 3). If they have trouble training the vanilla GAN on Pascal even on the 64*64 image size, this should be mentioned in the paper and be explained. I am concerned about this specially because CC-GAN almost matches the SSL-GAN baseline on STL-10, and CC-GAN2, to me, seems like a hacky way to improve upon the core CC-GAN idea. So it would be great to compare CC-GAN and SSL-GAN on some other dataset, even if it is a downsampled PASCAL dataset.
assistant
This paper proposes a method to incorporate super-resolution and inpainting in the GAN framework for semi-supervised learning using the GAN discriminative features on larger images. The core idea of the paper is not very novel. The usefulness of the GAN discriminative features for semi-supervised learning is already established in previous works such as CatGAN, DCGAN and Salimans et al. However this paper does a good job in actually getting the semi-supervised GAN framework working on larger images such as STL-10 and Pascal datasets using the proposed context conditioning approach, and achieves the state-of-the-art on these datasets. I think that the authors should provide the SSL-GAN baseline for the PASCAL dataset as it is very important to compare the contribution of the context conditioning idea with the standard way of using GAN for semi-supervised learning, i.e., SSL-GAN. I can*t see why the SSL-GAN can not be applied to the 64*64 and 96*96 version of the Pascal dataset (Table 3). If they have trouble training the vanilla GAN on Pascal even on the 64*64 image size, this should be mentioned in the paper and be explained. I am concerned about this specially because CC-GAN almost matches the SSL-GAN baseline on STL-10, and CC-GAN2, to me, seems like a hacky way to improve upon the core CC-GAN idea. So it would be great to compare CC-GAN and SSL-GAN on some other dataset, even if it is a downsampled PASCAL dataset.