Summary of Vector Quantization Prompting For Continual Learning, by Li Jiao et al.
Vector Quantization Prompting for Continual Learning
by Li Jiao, Qiuxia Lai, Yu Li, Qiang Xu
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 VQ-Prompt method addresses the challenges of catastrophic forgetting in continual learning by introducing Vector Quantization (VQ) into end-to-end training of a set of discrete prompts. This allows for optimized prompt selection with task loss, achieving effective abstraction of task knowledge for improved adaptation to new tasks. The approach outperforms state-of-the-art methods across various benchmarks under the class-incremental setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VQ-Prompt is a new way to learn from multiple tasks without forgetting what you already know. It uses special prompts that are designed to capture important information about each task, and then selects the best prompt for each task based on the input data. This helps the model adapt better to new tasks while still remembering old ones. The results show that VQ-Prompt works really well in many different situations. |
Keywords
» Artificial intelligence » Continual learning » Prompt » Quantization
