Summary of On Distilling the Displacement Knowledge For Few-shot Class-incremental Learning, by Pengfei Fang et al.
On Distilling the Displacement Knowledge for Few-Shot Class-Incremental Learning
by Pengfei Fang, Yongchun Qin, Hui Xue
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 Few-shot Class-Incremental Learning (FSCIL) addresses challenges in evolving data distributions and limited data acquisition. To mitigate catastrophic forgetting, knowledge distillation is employed to maintain learned knowledge. Our approach incorporates structural information between samples into knowledge distillation to improve feature representations. We introduce the Displacement Knowledge Distillation (DKD) method, which utilizes displacement rather than similarity between samples to enhance information density. The Dual Distillation Network (DDNet) applies traditional knowledge distillation to base classes and DKD to novel classes, allowing for complementary strengths. Instance-aware sample selection during inference adjusts dual branch weights, improving performance. Extensive testing on three benchmarks demonstrates state-of-the-art results for DDNet, with the proposed DKD method showing robustness and general applicability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about learning from small amounts of new data without forgetting what we already know. It’s a challenge because our brains (and computers) tend to forget old information when we learn new things. The authors developed a way to keep the important information using something called “knowledge distillation”. They also created a new method called Displacement Knowledge Distillation that helps us remember more effectively. This method is used in combination with another approach called the Dual Distillation Network, which helps the computer use both old and new knowledge together. The authors tested their ideas on several datasets and found that they worked really well, even when there was limited data. |
Keywords
» Artificial intelligence » Distillation » Few shot » Inference » Knowledge distillation
