Summary of Adaptive Retention & Correction: Test-time Training For Continual Learning, by Haoran Chen et al.
Adaptive Retention & Correction: Test-Time Training for Continual Learning
by Haoran Chen, Micah Goldblum, Zuxuan Wu, Yu-Gang Jiang
First submitted to arxiv on: 23 May 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 The proposed method, Adaptive Retention & Correction (ARC), tackles the issue of classification layer bias in continual learning by introducing a simple Out-of-Task Detection (OTD) method and two adaptive mechanisms: Adaptive Retention for dynamically tuning the classifier layer on past task data, and Adaptive Correction for revising predictions when classifying data from previous tasks. This approach is designed for memory-free environments but also shows promise in memory-based settings. The OTD method identifies samples from past tasks during testing, enabling the ARC to adaptively retain or correct its predictions accordingly. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Continual learning allows a model to learn and improve over time by processing a stream of incoming data. One challenge is that the classification layer can become biased towards the most recent task. To solve this issue, researchers have developed methods that incorporate data from past tasks during training. However, with the shift to memory-free environments, these approaches are no longer effective. The proposed ARC method focuses on the testing phase and introduces a simple OTD method to identify samples from past tasks. It then uses two adaptive mechanisms to adjust its predictions. |
Keywords
» Artificial intelligence » Classification » Continual learning
