Summary of A Bag Of Tricks For Few-shot Class-incremental Learning, by Shuvendu Roy et al.
A Bag of Tricks for Few-Shot Class-Incremental Learning
by Shuvendu Roy, Chunjong Park, Aldi Fahrezi, Ali Etemad
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed bag of tricks framework for few-shot class-incremental learning (FSCIL) combines six key techniques to improve stability, adaptability, and performance under a unified approach. The framework organizes these techniques into three categories: stability tricks, adaptability tricks, and training tricks. Stability tricks enhance the separation between learned class embeddings to mitigate forgetting, while adaptability tricks focus on effective learning of new classes. Training tricks improve overall performance without compromising stability or adaptability. The proposed method is evaluated on three benchmark datasets, CIFAR-100, CUB-200, and miniIMageNet, showing a substantial improvement in both stability and adaptability over prior works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a way to make machines learn new things while remembering what they already know. This is important because it helps the machine get better at doing its job without forgetting what it learned before. The researchers came up with six special techniques that work together to help the machine do this. They tested their approach on some pictures and words, and it did really well. Now, other people can use this method as a starting point for making machines even smarter. |
Keywords
* Artificial intelligence * Few shot
