Summary of Graph-based Unsupervised Disentangled Representation Learning Via Multimodal Large Language Models, by Baao Xie et al.
Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models
by Baao Xie, Qiuyu Chen, Yunnan Wang, Zequn Zhang, Xin Jin, Wenjun Zeng
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a novel bidirectional weighted graph-based framework for disentangled representation learning (DRL). Current DRL approaches assume statistical independence between semantic factors, but in reality, these factors often exhibit correlations. The proposed method uses a -VAE based module to extract initial nodes and the multimodal large language model (MLLM) to discover latent correlations, updating weighted edges. This framework successfully achieves fine-grained, practical, and unsupervised disentanglement, outperforming existing methods in experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to better identify patterns in data by learning about different underlying factors. Right now, most methods assume that these factors don’t relate to each other, but this isn’t always true. The authors created a new way to learn about these factors and how they’re connected, using a combination of two powerful tools: a special type of neural network called -VAE and a large language model. This new approach can help us better understand complex data and make more accurate predictions. |
Keywords
» Artificial intelligence » Large language model » Neural network » Representation learning » Unsupervised
