Asymmetric Beta Loss for Evidence-Based Safe Semi-Supervised Multi-Label Learning-Reference-Cited by-同舟云学术

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Asymmetric Beta Loss for Evidence-Based Safe Semi-Supervised Multi-Label Learning

Published:2024-08-24 Issue: Volume:8094 Page:1909-1920
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Liu Hao-Zhe¹^ORCID,Xie Ming-Kun¹^ORCID,Zong Chen-Chen¹^ORCID,Huang Sheng-Jun¹^ORCID

Affiliation:

1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671756

Reference44 articles.

1. Semi-Supervised Learning under Class Distribution Mismatch

2. NUS-WIDE

3. Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le. 2019. RandAugment: Practical automated data augmentation with a reduced search space. arxiv: 1909.13719 [cs.CV]

4. ImageNet: A large-scale hierarchical image database

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