Title: TEACHING GAN TO GENERATE PER-PIXEL ANNOTA-TION

Abstract: We propose a method for joint image and per-pixel annotation synthesis with GAN. We demonstrate that GAN has good high-level representation of target data that can be easily projected to semantic segmentation masks. This method can be used to create a training dataset for teaching separate semantic segmentation network. Our experiments show that such segmentation network successfully generalizes on real data. Additionally, the method outperforms supervised training when the number of training samples is small, and works on variety of different scenes and classes. The source code of the proposed method will be publicly available.

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Peer review (iclr2020):
The paper proposes a method for joint image and per-pixel annotation synthesis with GAN. Join image and per-pixel annotation can be a good idea however the paper needs significant improvement on literature review, model presentation and experiment result to be able to publish in ICLR. Detail comments are as following, 1. On the literature review part. And not only list the GAN papers that published recently ,but show an in depth understanding of these papers. Moreover, the review the works that is relevance to the proposed idea since there exists ton of papers related to GAN. 2. Model presentation part. How the loss function will be changed for this task. Formulate the problem and proposed idea mathematically. Detail explanation of the decoder part will also help. 3. Experiment. Two class problem is trivial task. how will the model perform in much boarder segmentation task? How was the quality of the images generated? How were results compared with other state of art?