Summary of Learning Multi-manifold Embedding For Out-of-distribution Detection, by Jeng-lin Li et al.
Learning Multi-Manifold Embedding for Out-Of-Distribution Detection
by Jeng-Lin Li, Ming-Ching Chang, Wei-Chao Chen
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Multi-Manifold Embedding Learning (MMEL) framework for detecting out-of-distribution (OOD) samples in real-world AI applications. Building upon recent advances in representation learning and latent embeddings, MMEL optimizes hypersphere and hyperbolic spaces jointly to enhance OOD detection. The proposed method generates representative embeddings and employs a prototype-aware scoring function to differentiate OOD samples, requiring no model retraining or significant OOD sample sets. Experiments on six open datasets demonstrate MMEL’s effectiveness in reducing false positives (FPR) while maintaining high AUC compared to state-of-the-art distance-based OOD detection methods. The framework’s ability to learn multiple manifolds and visualize OOD score distributions across datasets is also analyzed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to recognize pictures of dogs, but some pictures are weird or don’t have dogs at all. This paper helps computers do a better job of recognizing what’s normal and what’s not. They use new ways to understand how things are related, which makes it easier for the computer to spot when something is wrong. The authors tested their idea on six different groups of pictures and showed that it works really well. This means we can make computers that are more trustworthy in real-life situations. |
Keywords
» Artificial intelligence » Auc » Embedding » Representation learning
