Summary of Latentexplainer: Explaining Latent Representations in Deep Generative Models with Multimodal Large Language Models, by Mengdan Zhu et al.
LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multimodal Large Language Models
by Mengdan Zhu, Raasikh Kanjiani, Jiahui Lu, Andrew Choi, Qirui Ye, Liang Zhao
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper introduces LatentExplainer, a framework for automatically explaining latent variables in deep generative models like VAEs and diffusion models. LatentExplainer tackles three main challenges: inferring the meaning of latent variables, aligning explanations with inductive biases, and handling varying degrees of explainability. The approach perturbs latent variables, interpreting changes in generated data, and uses multi-modal large language models (MLLMs) to produce human-understandable explanations. The framework is evaluated on several real-world and synthetic datasets, demonstrating superior performance in generating high-quality explanations for latent variables. The results highlight the effectiveness of incorporating inductive biases and uncertainty quantification, significantly enhancing model interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper helps computers understand how they make decisions by explaining what’s going on inside deep learning models. These models are good at making fake data look real, but it’s hard to figure out why they’re making certain choices. The new system, called LatentExplainer, makes it easier to understand these models by analyzing the hidden patterns in the data. It does this by looking at how changing these patterns affects the generated data and using special language models to explain what’s happening in simple terms. The results show that this approach works well on real-world and made-up datasets, making it a valuable tool for understanding deep learning models. |
Keywords
* Artificial intelligence * Deep learning * Diffusion * Multi modal
