Summary of Continual Distillation Learning: Knowledge Distillation in Prompt-based Continual Learning, by Qifan Zhang et al.
Continual Distillation Learning: Knowledge Distillation in Prompt-based Continual Learning
by Qifan Zhang, Yunhui Guo, Yu Xiang
First submitted to arxiv on: 18 Jul 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 paper introduces the concept of continual distillation learning (CDL), which uses knowledge distillation to improve prompt-based continual learning (CL) models. The authors highlight the value of studying CDL, as using a larger vision transformer (ViT) leads to better performance in prompt-based CL. They also propose a novel method called Knowledge Distillation based on Prompts (KDP), which inserts globally accessible prompts into the frozen ViT backbone of the student model for knowledge distillation. The authors empirically show that KDP outperforms existing knowledge distillation methods in the CDL setup. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to make AI models better at learning new things. Right now, these models can get stuck if they have too much information and start making mistakes. To fix this, the researchers came up with a way to “distill” or transfer knowledge from one model to another. This helps smaller models learn more efficiently. They tested their method on a specific type of AI model called ViT (vision transformer) and found that it improved performance. The goal is to make AI systems more efficient and accurate, especially when they need to learn new things. |
Keywords
* Artificial intelligence * Continual learning * Distillation * Knowledge distillation * Prompt * Student model * Vision transformer * Vit
