{"id":69120,"date":"2024-09-23T15:41:40","date_gmt":"2024-09-23T07:41:40","guid":{"rendered":"https:\/\/inventec2.mjitec.tw\/?page_id=69120"},"modified":"2024-09-23T18:11:56","modified_gmt":"2024-09-23T10:11:56","slug":"learning-multi-manifold-embedding-for-out-of-distribution-detection","status":"publish","type":"page","link":"https:\/\/inventec2.mjitec.tw\/zh-hans\/ai\/learning-multi-manifold-embedding-for-out-of-distribution-detection\/","title":{"rendered":"Learning Multi-Manifold Embedding for Out-Of-Distribution Detection"},"content":{"rendered":"

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Learning Multi-Manifold Embedding for Out-Of-Distribution Detection <\/h2>\n\t <\/div><\/div>[vc_column_text css=”.vc_custom_1727076166097{margin-bottom: 20px !important;}”]2024 European Conference on Computer Vision Workshop (ECCVW2024)[\/vc_column_text][vc_column_text css=”.vc_custom_1660542835761{margin-bottom: 5px !important;}”]<\/p>\n
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[\/vc_column_text][vc_column_text css=”.vc_custom_1727076182809{margin-bottom: 20px !important;}”]Jeng-Lin Li, Ming-Ching Chang, and Wei-Chao Chen[\/vc_column_text][vc_column_text css=”.vc_custom_1727078784479{margin-bottom: 5px !important;}”]<\/p>\n

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\u6982\u8981 <\/h2>\n\t <\/div><\/div>[\/vc_column_inner][vc_column_inner el_class=”m_p” width=”2\/3″][vc_column_text css=””]Detecting out-of-distribution (OOD) samples is crucial for trustworthy AI in real-world applications. Leveraging recent advances in representation learning and latent embeddings, Various scoring algorithms estimate distributions beyond the training data. However, a single embedding space falls short in characterizing in-distribution data and defending against diverse OOD conditions.<\/p>\n

This paper introduces a novel Multi-Manifold Embedding Learning (MMEL) framework, optimizing hypersphere and hyperbolic spaces jointly for enhanced OOD detection. MMEL generates representative embeddings and employs a prototype-aware scoring function to differentiate OOD samples.<\/p>\n

Experiments on six open datasets demonstrate MMEL’s significant reduction in FPR while maintaining a high AUC compared to state-of-the-art distance-based OOD detection methods.<\/p>\n

Notably, enrolling ten OOD samples without retraining achieves comparable FPR and AUC to modern outlier exposure methods using 80 million outlier samples for model training.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row][vc_column]

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