Summary of Colidr: Concept Learning Using Aggregated Disentangled Representations, by Sanchit Sinha et al.
CoLiDR: Concept Learning using Aggregated Disentangled Representations
by Sanchit Sinha, Guangzhi Xiong, Aidong Zhang
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 method called CoLiDR that unifies the explanation of deep neural networks’ behavior through human-understandable concepts and disentangled generative factors. The approach uses a disentangled representation learning setup to learn mutually independent generative factors, which are then aggregated into human-understandable concepts using a novel aggregation/decomposition module. Experiments show that CoLiDR successfully aggregates disentangled generative factors into concepts while maintaining parity with state-of-the-art concept-based approaches on four challenging datasets. The paper’s contributions include the development of a flexible method suitable for various types of data and the demonstration of advantages over commonly used concept-based models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how deep neural networks work by using ideas from two different areas: disentangling data into its underlying generative factors, and explaining model behavior through human-understandable concepts. The authors combine these ideas to create a new method called CoLiDR that can explain complex data in a way that’s easy for humans to understand. They test this method on four datasets and show that it works well compared to other methods. This is important because it means we can use CoLiDR to explain how deep neural networks make decisions, which can help us trust them more. |
Keywords
» Artificial intelligence » Representation learning
