Summary of Covariance-based Space Regularization For Few-shot Class Incremental Learning, by Yijie Hu et al.
Covariance-based Space Regularization for Few-shot Class Incremental Learning
by Yijie Hu, Guanyu Yang, Zhaorui Tan, Xiaowei Huang, Kaizhu Huang, Qiu-Feng Wang
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper tackles the challenging problem of Few-shot Class Incremental Learning (FSCIL), where models must learn new classes with limited labeled data while retaining knowledge of previously learned base classes. To overcome overfitting and catastrophic forgetting, recent approaches have used prototype-based methods to constrain base class distributions and learn discriminative representations. However, these methods still struggle with ill-divided feature spaces, leading to confusion between new and old classes or poor separation among new classes. The proposed approach addresses this issue by constraining the span of each class distribution from a covariance perspective, using a simple yet effective covariance constraint loss. Additionally, a perturbation approach is introduced to encourage samples to be away from weighted distributions of other classes, establishing explicit boundaries between new and old classes. This approach can be easily integrated into existing FSCIL methods to boost performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in machine learning called Few-shot Class Incremental Learning (FSCIL). Imagine you’re trying to teach a computer to recognize new objects or animal species, but it only has a few examples to learn from. The computer needs to remember what it already knows about older objects while also learning about the new ones. Right now, computers can get confused and forget what they knew about old objects when they’re learning about new ones. This paper presents a new way for computers to learn about new classes while remembering old ones by controlling how much each class varies from others. It also adds some noise to the training data to help the computer establish clear boundaries between different classes. The results show that this approach works really well and can even beat other state-of-the-art methods. |
Keywords
» Artificial intelligence » Few shot » Machine learning » Overfitting
