Summary of Knowledge Adaptation Network For Few-shot Class-incremental Learning, by Ye Wang et al.
Knowledge Adaptation Network for Few-Shot Class-Incremental Learning
by Ye Wang, Yaxiong Wang, Guoshuai Zhao, Xueming Qian
First submitted to arxiv on: 18 Sep 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 The proposed KANet model tackles the challenge of few-shot class-incremental learning by employing a foundation model, CLIP, as the network pedestal to provide general representations for each class. To generate more reliable instance representations, it introduces a Knowledge Adapter (KA) module that summarizes data-specific knowledge from training data and fuses it into the general representation. Additionally, Incremental Pseudo Episode Learning (IPEL) is proposed to tune the learned knowledge from base classes to upcoming classes. The KANet model achieves competitive performance on various datasets, including CIFAR100, CUB200, and ImageNet-R. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KANet is a new way for computers to learn about lots of different things. It helps them recognize new things when they only have a few examples to work with. This is useful because in real life, we don’t always have a lot of information about something before we try to understand it. The KANet model uses an existing foundation model called CLIP as a starting point and then adds some extra steps to make the learning more accurate. It’s like building on top of what you already know to make it stronger. This new approach works well on many different types of data, including pictures of animals and objects. |
Keywords
» Artificial intelligence » Few shot
