Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: This work proposed a generative data augmentation method for image segmentation. Two GANs are designed to generate and compose images for sky-cloud image segmentation.
strengths_and_weaknesses: ### Strengths 

1. Clear writing. This paper provides a well-organized context for reviewers to understand the generative data augmentation problem. Methods are properly motivated.

2. Visually compelling results. Generated data augmentations are visually close to the samples from the original dataset.

### Weaknesses

1. Comparison. Given the purpose of this work is to augment resource-constrained image segmentation task, it could make this work more convincing to train a small model over the original data and the joint of the original dataset and augmented dataset.

2. Paper organization. The writing is overall clear, but the paper can be organized better. Authors are expected to streamline the introduction section and give more attention to the method description.
suggested_changes: Authors are encouraged to improve this work from these aspects.

1. Include a comparison of the performance of a model trained on the augmented data vs. a model trained on the original data, to demonstrate the effectiveness of the proposed method for resource-constrained image segmentation.

2. Streamline the introduction section better to highlight the motivation and contribution of the proposed method, and provide more detail and clarity in the method description section.
review_title: null

Meta-review: metareview: The paper proposes a generative data augmentation method for image segmentation by training two separate GANs to generate foreground cloud masks and background clear sky images, respectively, and then merging them together. The paper is well-written and provides visually compelling results. However, the paper could be organized better, and the proposed method could benefit from a comparison of its performance against a model trained solely on the original data.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to revise