Summary of Envisioning Outlier Exposure by Large Language Models For Out-of-distribution Detection, By Chentao Cao et al.
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection
by Chentao Cao, Zhun Zhong, Zhanke Zhou, Yang Liu, Tongliang Liu, Bo Han
First submitted to arxiv on: 2 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 The proposed paper tackles the challenge of zero-shot out-of-distribution (OOD) sample detection using vision-language models like CLIP. Existing methods rely on text-based classifiers with closed-set labels, limiting the capability to recognize samples from large and open label spaces. The authors propose Envisioning Potential Outlier Exposure (EOE), leveraging large language models’ expert knowledge and reasoning capabilities without access to actual OOD data. EOE can be generalized to different tasks, including far, near, and fine-grained OOD detection. To achieve this, the authors design LLM prompts based on visual similarity to generate potential outlier class labels and a new score function based on potential outlier penalty to distinguish hard OOD samples effectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes Envisioning Potential Outlier Exposure (EOE), which uses large language models’ expert knowledge and reasoning capabilities without access to actual out-of-distribution (OOD) data. This method can be used for different tasks, including far, near, and fine-grained OOD detection. The authors also design prompts based on visual similarity and a new score function to help distinguish hard OOD samples. |
Keywords
» Artificial intelligence » Zero shot
