Summary of Resultant: Incremental Effectiveness on Likelihood For Unsupervised Out-of-distribution Detection, by Yewen Li et al.
Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection
by Yewen Li, Chaojie Wang, Xiaobo Xia, Xu He, Ruyi An, Dong Li, Tongliang Liu, Bo An, Xinrun Wang
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 investigates unsupervised out-of-distribution (U-OOD) detection using deep generative models (DGMs). A natural detector is the likelihood function estimated by DGMs, but its performance is limited in certain benchmarks. The authors explore various detectors based on DGMs and find that while they excel in “hard” benchmarks, they struggle to surpass or match the performance of likelihood in “non-hard” cases. To address this issue, the paper investigates two directions for improving the detection performance: alleviating latent distribution mismatch and calibrating dataset entropy-mutual integration. Two techniques are applied to each direction, resulting in a final method called Resultant. Experimental results show that Resultant is a new state-of-the-art U-OOD detector with incremental effectiveness on likelihood. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how computers can tell when they’re seeing something unusual or “out of the ordinary”. This is important because it helps them make better decisions. The current best method uses a special kind of computer model called a deep generative model, but it doesn’t always work well. Researchers are trying to improve this method by making some changes. They found that if they adjust two things – how the model represents hidden information and how it measures the “unusualness” of something – they can make the model better at detecting unusual data. They tested their new approach, called Resultant, and found that it works really well. |
Keywords
» Artificial intelligence » Generative model » Likelihood » Unsupervised
