Few-Shot Class-Incremental Learning via Feature Space Composition

By Hanbin Zhao, Yongjian Fu, Xuewei Li, Songyuan Li, Bourahla Omar, Xi Li. 2020

6 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. Da-Wei Zhou
2. Songlin Dong
3. Hanbin Zhao
4. Xiaopeng Hong
5. Xiaoyu Tao
6. Han-Jia Ye
7. Chi Zhang
8. Xinyuan Chang

Citations in the corpus

• Few-Shot Class-Incremental Learning via Relation Knowledge Distillation (2021, methodology): “In the C-way K-shot FSCIL setting (Tao et al. 2020b; Zhao et al. 2020), the number of classes in C ( t ) is C and the number of training samples per class is K for each X ( t ) ; t > 1 .”
• Few-Shot Incremental Learning with Continually Evolved Classifiers (2021, background): “Tao et al. [39] propose a neural gas network to preserve the topology of the features in the base and new classes for the FSCIL task.”
• Memory-Efficient Class-Incremental Learning for Image Classification (2020, background): “Recently, there have been a lot of research works on incremental learning with deep models [33]–[46].”