Overcoming Catastrophic Forgetting for Multi-Label Class-Incremental Learning-Reference-Cited by-同舟云学术

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Overcoming Catastrophic Forgetting for Multi-Label Class-Incremental Learning

Published:2024-01-03 Issue: Volume:33 Page:2378-2387
ISSN:
Container-title:2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
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Author:

Song Xiang¹,Shu Kuang²,Dong Songlin³,Cheng Jie⁴,Wei Xing¹,Gong Yihong¹

Affiliation:

1. Xi’an Jiaotong Univerisity,School of Software Engineering,Xi’an,China

2. Central Southern China Electric Power Design Institute Co., ltd. of China Power Engineering Consulting Group,Wuhan,China

3. Xi’an Jiaotong University,College of Artificial Intelligence,Xi’an,China

4. Huawei Base,Bantian, Shenzhen,China

Funder

Research and Development

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10483279/10483282/10484278.pdf?arnumber=10484278

Reference51 articles.

1. Memory Aware Synapses: Learning What (not) to Forget

2. Asymmetric Loss For Multi-Label Classification

3. Dark experience for general continual learning: a strong, simple baseline;Buzzega

4. End-to-End Incremental Learning

5. Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition

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1. Anchor Your Embeddings Through the Storm: Mitigating Instance-to-Document Semantic Gap;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

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