Summary of Aha: Human-assisted Out-of-distribution Generalization and Detection, by Haoyue Bai et al.
AHA: Human-Assisted Out-of-Distribution Generalization and Detection
by Haoyue Bai, Jifan Zhang, Robert Nowak
First submitted to arxiv on: 10 Oct 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 This paper proposes a novel approach, Adaptive Human-Assisted OOD learning (AHA), to address both out-of-distribution (OOD) generalization and detection in machine learning models. AHA is an integrated framework that uses human-assistance to label data in the wild, maximizing utility with a fixed labeling budget. The algorithm first identifies a maximum disambiguation region where semantic and covariate OOD data roughly equalize, then annotates within this region using noisy binary search and human feedback. Experimental results show that AHA outperforms state-of-the-art methods without human assistance in both OOD generalization and detection with only a few hundred human annotations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn better by working together with humans to label data. When machines are used in real-life situations, they often encounter new types of information that are different from what they were trained on. This can make it hard for them to generalize correctly or detect when something is unusual. The authors developed a new method called AHA (Adaptive Human-Assisted OOD learning) that uses human help to label data and makes machines better at handling these situations. |
Keywords
» Artificial intelligence » Generalization » Machine learning
