Summary of Consistent Prompting For Rehearsal-free Continual Learning, by Zhanxin Gao et al.
Consistent Prompting for Rehearsal-Free Continual Learning
by Zhanxin Gao, Jun Cen, Xiaobin Chang
First submitted to arxiv on: 13 Mar 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 proposes a novel approach, called Consistent Prompting (CPrompt), to improve the effectiveness of prompt-based methods in continual learning. These methods typically rely on frozen pre-trained models and learn task-specific prompts and classifiers efficiently. However, existing approaches suffer from inconsistencies between training and testing, which limits their performance. The authors identify two types of inconsistency: classifier inconsistency, where test predictions are made from all classifiers while only focusing on the current task classifier during training; and prompt inconsistency, where the prompt selected during testing may not correspond to the one associated with the task during training. To address these issues, CPrompt exposes all existing classifiers to prompt training, achieving classifier consistency learning, and proposes prompt consistency learning to enhance prediction robustness and boost prompt selection accuracy. Experimental results show that CPrompt surpasses its prompt-based counterparts and achieves state-of-the-art performance on multiple continual learning benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Continual learning allows models to adapt to new data without forgetting what they already know. This is important because the world is constantly changing, and models need to be able to keep up. One way to do this is by using “prompts” – special instructions that help the model understand what it’s supposed to be doing. The problem is that current methods for creating these prompts aren’t very good, and they can actually make things worse. This paper proposes a new method called Consistent Prompting (CPrompt) that tries to fix this issue. It does this by making sure that the prompts used during training are the same as the ones used during testing. This helps the model learn more consistently and makes it better at adapting to new data. The authors tested CPrompt on several different tasks and found that it worked much better than other methods. |
Keywords
* Artificial intelligence * Continual learning * Prompt * Prompting
