Summary of Zero-shot Object-level Ood Detection with Context-aware Inpainting, by Quang-huy Nguyen et al.
Zero-shot Object-Level OOD Detection with Context-Aware Inpainting
by Quang-Huy Nguyen, Jin Peng Zhou, Zhenzhen Liu, Khanh-Huyen Bui, Kilian Q. Weinberger, Dung D. Le
First submitted to arxiv on: 5 Feb 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 In this research paper, scientists tackle a critical challenge in machine learning: detecting when a new object doesn’t belong to the same category as previously seen objects. This is crucial for cloud services or pre-trained models that lack access to their training data. To address this issue, they propose an innovative approach called RONIN, which leverages off-the-shelf diffusion models and inpainting techniques. By conditioning the inpainting process with predicted labels, RONIN can effectively distinguish between in-distribution (ID) and out-of-distribution (OOD) samples. The authors demonstrate the effectiveness of RONIN through extensive experiments across various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists try to figure out when something new doesn’t fit into a group we already know about. This is important because sometimes we don’t have access to the original information that taught us how things work together. They created a new way called RONIN that uses special computer programs and techniques to help us tell apart what belongs and what doesn’t belong. By using these tools, RONIN can make sure we’re making good choices about whether something is like something else or not. The scientists tested RONIN on many different sets of data and showed it works well. |
Keywords
* Artificial intelligence * Machine learning
