Summary of Prior-free Balanced Replay: Uncertainty-guided Reservoir Sampling For Long-tailed Continual Learning, by Lei Liu and Li Liu and Yawen Cui
Prior-free Balanced Replay: Uncertainty-guided Reservoir Sampling for Long-Tailed Continual Learning
by Lei Liu, Li Liu, Yawen Cui
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 In this paper, researchers tackle the issue of catastrophic forgetting in continual learning (CL), specifically in Long-Tailed Continual Learning (LTCL) scenarios where data streams exhibit long-tailed distributions. Existing solutions require prior knowledge of label distributions to achieve re-balance training, which is often impractical in real-world scenarios. To address this challenge, the authors propose a novel Prior-free Balanced Replay (PBR) framework that learns from LTCL data streams with minimal forgetting. The PBR framework incorporates an uncertainty-guided reservoir sampling strategy to prioritize rehearsing minority data without prior information. Additionally, it includes two prior-free components: boundary constraint for preserving uncertain boundary supporting samples and prototype constraint for maintaining consistency of learned class prototypes. The authors evaluate their approach on three standard LTCL benchmarks, demonstrating superior performance compared to existing CL methods and previous state-of-the-art (SOTA) LTCL approaches in both task- and class-incremental learning settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to make machines learn new things without forgetting what they already know. It’s especially important when the new things are rare, like minority classes. The authors came up with a new way called Prior-free Balanced Replay (PBR) that helps machines remember both common and rare things. They tested it on lots of data and showed that it works better than other ways to learn. |
Keywords
* Artificial intelligence * Continual learning
