Summary of Contrast: Continual Multi-source Adaptation to Dynamic Distributions, by Sk Miraj Ahmed et al.
CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions
by Sk Miraj Ahmed, Fahim Faisal Niloy, Xiangyu Chang, Dripta S. Raychaudhuri, Samet Oymak, Amit K. Roy-Chowdhury
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel method for adapting to dynamic data distributions is proposed, which combines multiple source models to optimize performance. The Continual Multi-source Adaptation to Dynamic Distributions (CONTRAST) approach efficiently computes optimal combination weights and identifies which model parameters to update based on correlation with the target data. This allows for prioritization of updates to the least prone to forgetting, mitigating the issue of “forgetting” source model parameters. Experimental results demonstrate that combining multiple source models performs at least as well as the best individual source, and performance does not degrade over time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Adapting to changing data distributions is a big challenge in machine learning. One way to solve this problem is by using many different models together. This approach is called model ensemble. However, it can be hard when we only have small batches of new data and don’t know the original source. A new method called CONTRAST solves this problem by combining multiple source models to adapt to changing test data distributions. It efficiently computes the best combination weights and updates the least prone to forgetting, so we don’t forget what we learned from other models. |
Keywords
* Artificial intelligence * Machine learning
