LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multi-modal Foundation Models
Authors: Mengdan Zhu, Raasikh Kanjiani, Jiahui Lu, Andrew Choi, Qirui Ye, Liang Zhao


Reviewer paper summaries: - This paper introduces LatentExplainer, a framework for automatically generating semantically meaningful explanations of latent variables in deep generative models.
Summary of strengths: Exploring an interesting question, attempting to leverage the capabilities of multimodal large models to explain latent variables.
Summary of weaknesses: - The system framework and experiments do not specifically explain how to perturb the latent variables.
- There are no concrete examples to visually demonstrate how to automatically generate prompts.
- The similarity in line 405 does not explain how it is calculated.
- There is no comparison with other related methods.
Comments, suggestions and typos: - The writing has a lot of room for improvement, with many redundant parts: lines 86-101, 102-132, and 226-237 contain unnecessary repetitions of the same content.
Overall assessment scores: 2