Few-Shot Class Incremental Learning with Generative Feature Replay

By Abhilash Shankarampeta, Koichiro Yamauchi. 2021

12 citations recorded by the source corpus.

The humans can learn novel concepts from only a few examples effortlessly and learn additional tasks without forgetting previous ones. Making machines to learn incrementally from only a few instances is very challenging due to catastrophic forgetting between new and previously learned tasks; this can be solved by generative image replay. However, image generation with only a few examples is a challenging task. In this work, we propose a feature replay approach instead of image replay for few-shot learning scenarios. A feature extractor with feature distillation is combined with feature replay at the classifier level to tackle catastrophic forgetting.

Subfields: Artificial Intelligence, Cancer Research

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In Artificial Intelligence

1. Jingshan Tang
2. Yuanhao Yu
3. Li Gu
4. Huan Liu
5. Zhixiang Chi
6. Yang Wang

Citations in the corpus

• Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay (2022, background): “The first line of research [10,25,24] requires a generator to be trained on the original”