Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The paper explores the image manipulations learned by GANSpace, a state-of-the-art method based on PCA, and compares it to a modified version that utilizes Independent Component Analysis (ICA) to address the problem of entanglement in image manipulations. The authors demonstrate that ICA can produce more disentangled manipulations and a wider variety of transformations compared to PCA.
strengths_and_weaknesses: ### Strengths:

1. Effective image manipulations. The paper demonstrates that GANSpace produces a wide range of high-quality image manipulations, making it a valuable tool for controlled data generation with GANs.

2. Replacement of PCA with ICA. The paper proposes replacing PCA with ICA, which improves the quality and disentanglement of manipulations.

3. Visual and quantitative evaluation. The paper performs both a visual and quantitative evaluation of GANSpace on two different GAN models, providing comprehensive assessment results.

### Weaknesses:

1. Limited explanation of ICA: The paper provides limited explanation of ICA and how it improves the quality and disentanglement of manipulations compared to PCA. The authors could have provided more detail on the mathematical concepts and algorithms used in ICA.

2. Writing organization. The results session is too intense to read. Authors are expected to reorganize this session into categorical explanations of their findings
suggested_changes: 1. Provide a more detailed explanation of ICA and its advantages over PCA, to make it more accessible to readers.

2. Reorganize the results section. The review suggests that the results section is too intense to read and could benefit from being reorganized into categorical explanations. The reviewer could provide specific suggestions on how the authors could reorganize the section to make it more readable and accessible.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The paper focuses on investigating image manipulation learned by GANSpace. The main idea is to use PCA and ICA on middle features of the generators then using the least squares method to obtain the matrix of transformation in the original latent space.
strengths_and_weaknesses: # Strengths
- The proposed method is new and interpretable since it is based on linear models and generalized linear models.
- The paper shows experiments on various architecture of GANs e.g., StyleGAN2.
- Qualitative results are shown to verify the proposed method.

# Weaknesses
- The paper should move the description of the method from the Appendix to the main text. 
suggested_changes: - The paper should move the description of the method from the Appendix to the main text.
review_title: null

Decision: Invite to archive