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 to synthesize the training data for semantic segmentation. The method adds an additional network to GAN-based image generator which annotates pixel masks for targeted features in generated images. Another semantic segmentation network is trained by both the original training data and many synthesized data from the proposed network. Overall, there is very few information in the main text. Especially, there is no implementation details of the additional decoder, and no explanation about features fed to the decoder. I*d say that it is hard to reproduce the results in this paper. Some experiments look unfair. E.g., the proposed method additionally uses 20 generated images with human annotations, which means that the proposed method has twice many opportunity to obtain information from human. For fair experiment, these images should also be used to train the baseline systems. Some experimental results only show its output images without any statistics and/or explanations. It is hard to grasp what is the important information of these results.