Summary of Uncertainty Quantification in Continual Open-world Learning, by Amanda S. Rios et al.
Uncertainty Quantification in Continual Open-World Learning
by Amanda S. Rios, Ibrahima J. Ndiour, Parual Datta, Jaroslaw Sydir, Omesh Tickoo, Nilesh Ahuja
First submitted to arxiv on: 21 Dec 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 The paper proposes a novel method called COUQ (Continual Open-world Uncertainty Quantification) that enables AI agents to autonomously adapt to novel data encountered after deployment. The traditional approach in continual learning relies on novelty and labeling oracles, which is unrealistic. COUQ addresses this challenge by introducing an iterative uncertainty estimation algorithm designed for generalized continual open-world multi-class settings. The method is evaluated on sub-tasks such as novelty detection, active learning, and pseudo-labeling for semi-supervised CL. Experimental results demonstrate the effectiveness of COUQ across multiple datasets, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI agents should be able to adapt to new things they encounter after being deployed. Right now, AI systems need help from humans or special labeling tools to learn from new data. This paper suggests a way for AI agents to figure out what’s new and what they don’t know on their own. The method is called COUQ (Continual Open-world Uncertainty Quantification) and it helps the AI agent learn from new data without human help. The researchers tested this method on different tasks and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Active learning » Continual learning » Novelty detection » Semi supervised
