Review for NeurIPS paper: Learning Semantic-aware Normalization for Generative Adversarial Networks

Summary and Contributions: This paper improves the StyleGAN-based image generation model by disentangling semantics based on a learnable semantics grouping operation, where the styles of the intra-group features are controlled by group-wise adaptive instance normalization and the overall features are re-balanced by inter-group adaptive group normalization. Quantitative and qualitative evaluations show certain improvements over existing methods. Strengths: - The quantitative evaluations and ablation study validates the effectiveness of the proposed improvements. The most critical limitation of this work is its novelty and theoretical soundness. However, similarity between layers of a convolutional kernel may not indicate consistent similarity between corresponding feature channels.