Summary of Inconsistency-based Data-centric Active Open-set Annotation, by Ruiyu Mao et al.
Inconsistency-Based Data-Centric Active Open-Set Annotation
by Ruiyu Mao, Ouyang Xu, Yunhui Guo
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper proposes a novel approach to active learning called NEAT, which addresses the issue of unknown classes in unlabeled data. Current active learning methods assume all data comes from predefined known classes, but this is often not valid in practical situations. NEAT actively annotates open-set data by identifying known classes from the unlabeled pool and selecting informative samples based on a consistency criterion that measures inconsistencies between model predictions and local feature distribution. Unlike previous methods, NEAT is computationally efficient and achieves significantly better performance for active open-set annotation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NEAT is a new way to learn with unknown data. Right now, we have ways to train machines without labeling all the data, but these methods assume we know what kinds of things are in the unlabeled pool. That’s not always true! Sometimes there are surprises in the data that can make our machine learning models perform badly. NEAT helps by finding the known classes in the unknown data and choosing which samples to label first. |
Keywords
* Artificial intelligence * Active learning * Machine learning
