Summary of Learning Conditional Invariances Through Non-commutativity, by Abhra Chaudhuri et al.
Learning Conditional Invariances through Non-Commutativity
by Abhra Chaudhuri, Serban Georgescu, Anjan Dutta
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper proposes a new approach to learning conditional invariances, which are used to filter out domain-specific random variables as distractors. The method relaxes the traditional invariance criterion to be non-commutatively directed towards the target domain, allowing for more effective adaptation across domains. Experimental results demonstrate that this non-commutative invariance (NCI) approach can leverage source domain samples to meet the sample complexity needs of learning optimal encoders for domain adaptation, surpassing state-of-the-art (SOTA) methods by up to 2%. The paper’s theory and experiments also show that NCI can bring the divergence between domains down to zero, leading to a stricter bound on the target risk. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how computers can learn to ignore certain information when it comes from a different source. This is important for tasks like translating languages or recognizing objects in pictures. The authors propose a new way of doing this called non-commutative invariance (NCI). They show that NCI can help computers adapt to new situations more easily, and that it can even perform better than other methods in some cases. This could be useful for applications like self-driving cars or medical diagnosis. |
Keywords
* Artificial intelligence * Domain adaptation
