DETA: Denoised Task Adaptation for Few-Shot Learning
Author:
Affiliation:
1. University of Electronic Science and Technology of China (UESTC)
2. UESTC,Shenzhen Institute for Advanced Study
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10376473/10376477/10378026.pdf?arnumber=10378026
Reference61 articles.
1. A closer look at memorization in deep networks;Arpit
2. Enhancing Few-Shot Image Classification with Unlabelled Examples
3. Improved Few-Shot Visual Classification
4. Flex: Unifying evaluation for few-shot nlp;Bragg;NeurIPS,2021
5. Emerging Properties in Self-Supervised Vision Transformers
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1. Hierarchical contrastive representation for zero shot learning;Applied Intelligence;2024-07-12
2. A Progressive Placeholder Learning Network for Multimodal Zero-Shot Learning;IEEE Transactions on Multimedia;2024
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