Summary of Cross-domain Open-world Discovery, by Shuo Wen and Maria Brbic
Cross-domain Open-world Discovery
by Shuo Wen, Maria Brbic
First submitted to arxiv on: 17 Jun 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 A novel machine learning approach called CROW is proposed to address the challenge of assigning samples to seen classes and discovering unseen classes in a cross-domain open-world setting. The goal is to handle both categorical shifts, where new classes emerge, as well as distribution shifts caused by feature distributions different from those used during training. CROW utilizes a prototype-based strategy, which involves clustering and matching with previously seen classes, followed by fine-tuning the representation space using an objective designed for cross-domain open-world discovery. This approach is shown to outperform alternative baselines on image classification benchmark datasets, achieving an average performance improvement of 8% across 75 experimental settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists developed a new way to help machines learn and understand when they encounter new data that’s different from what they were trained on. They created a system called CROW that can identify both old and new categories, as well as changes in how features are distributed within those categories. This is important because it allows machines to learn and adapt more effectively in real-world situations. |
Keywords
» Artificial intelligence » Clustering » Fine tuning » Image classification » Machine learning
