Learning Classifier Synthesis for Generalized Few-Shot Learning

By Han-Jia Ye, Hexiang Hu, De-chuan Zhan, Fei Sha. 2019

11 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xinting Hu
2. Hanwang Zhang
3. Kaihua Tang
4. Jingyuan Chen
5. Philip S. Yu
6. Congying Xia
7. Chunyan Miao
8. Hoang Nguyen

Citations in the corpus

• CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection (2020, background): “Ye et al. (2019) proposes a learning framework, Classifier Synthesis Learning (CAS-TLE), which learns how to synthesize calibrated few-shot classifiers in addition to the classifiers of seen classes, leveraging a shared neural dictionary between seen classes and novel classes.”
• Learning to Segment the Tail (2020, methodology): “For few-shot learning, meta-learning based works transfer the learning-to-learn knowledge through feature representation [29, 32, 20], classifier weights [46, 8], and the regression of model parameters [42, 43] from the data-rich base classes, to obtain a good model initialization for the datapoor n”