Semantic-based Selection, Synthesis, and Supervision for Few-shot Learning

Author:

Lu Jinda1ORCID,Wang Shuo1ORCID,Zhang Xinyu1ORCID,Hao Yanbin1ORCID,He Xiangnan1ORCID

Affiliation:

1. University of Science and Technology of China, Hefei, China

Funder

National Key Research and Development Program of China

Publisher

ACM

Reference59 articles.

1. Mixture-based Feature Space Learning for Few-shot Image Classification

2. L Bertinetto , J Henriques , P Torr , and A Vedaldi . 2019 . Meta-learning with differentiable closed-form solvers . In International Conference on Learning Representations (ICLR), 2019. International Conference on Learning Representations. L Bertinetto, J Henriques, P Torr, and A Vedaldi. 2019. Meta-learning with differentiable closed-form solvers. In International Conference on Learning Representations (ICLR), 2019. International Conference on Learning Representations.

3. Knowledge Graph Transfer Network for Few-Shot Recognition

4. Wei-Yu Chen , Yen-Cheng Liu , Zsolt Kira , Yu-Chiang Frank Wang , and Jia-Bin Huang . 2019 b. A Closer Look at Few-shot Classification . In International Conference on Learning Representations. Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang. 2019b. A Closer Look at Few-shot Classification. In International Conference on Learning Representations.

5. Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning

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