Summary of Adapter-enhanced Semantic Prompting For Continual Learning, by Baocai Yin et al.
Adapter-Enhanced Semantic Prompting for Continual Learning
by Baocai Yin, Ji Zhao, Huajie Jiang, Ningning Hou, Yongli Hu, Amin Beheshti, Ming-Hsuan Yang, Yuankai Qi
First submitted to arxiv on: 15 Dec 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 This paper proposes a novel lightweight continual learning (CL) framework called Adapter-Enhanced Semantic Prompting (AESP), which integrates prompt tuning and adapter techniques to address the challenge of catastrophic forgetting in CL. The approach designs semantic-guided prompts to enhance visual feature generalization and utilizes adapters to efficiently fuse semantic information, aiming to learn more adaptive features for the continual learning task. Additionally, a novel matching mechanism is developed to choose the right task prompt for feature adaptation. Experimental results on three CL datasets demonstrate the framework’s favorable performance across multiple metrics, showing its potential for advancing CL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn new things from changing data streams without forgetting what we already know. It solves a big problem in this area called catastrophic forgetting. The usual ways to solve this require a lot of memory or extra parts in the model. This paper introduces a new way that is lightweight and efficient, using something called prompt tuning and adapter techniques. They design special prompts to help features adapt to changing data, and use adapters to combine information from different sources. They also develop a way to choose the right prompt for adapting features. The results show this approach works well on three different datasets. |
Keywords
» Artificial intelligence » Continual learning » Generalization » Prompt » Prompting
