Summary of Atlas: Adapter-based Multi-modal Continual Learning with a Two-stage Learning Strategy, by Hong Li et al.
ATLAS: Adapter-Based Multi-Modal Continual Learning with a Two-Stage Learning Strategy
by Hong Li, Zhiquan Tan, Xingyu Li, Weiran Huang
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed adapter-based two-stage learning paradigm presents a promising approach to alleviate catastrophic forgetting in vision-and-language models. Building upon parameter-efficient modules like adapters and prompts, this method consists of experience-based learning and novel knowledge expansion, enabling the model to fully utilize its experience and compensate for novel information. The scheme is tested on both uni-modal and multi-modal tasks, demonstrating its proficiency in continual learning. Furthermore, it enhances generalization capabilities for downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for machines to learn from experience and adapt to new situations without forgetting what they already know. By using special modules called adapters and prompts, the model can update its knowledge without starting from scratch each time. This is important because most machine learning models are trained on a specific task or dataset and then forget how to do other tasks or datasets once they’re done with the original one. The proposed method shows that it’s possible to learn from experience and expand our knowledge while minimizing forgetting. |
Keywords
» Artificial intelligence » Continual learning » Generalization » Machine learning » Multi modal » Parameter efficient
