Summary of Hide-pet: Continual Learning Via Hierarchical Decomposition Of Parameter-efficient Tuning, by Liyuan Wang et al.
HiDe-PET: Continual Learning via Hierarchical Decomposition of Parameter-Efficient Tuning
by Liyuan Wang, Jingyi Xie, Xingxing Zhang, Hang Su, Jun Zhu
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 framework for continual learning (CL) with pre-trained models (PTMs) and parameter-efficient tuning (PET) techniques aims to sustain the advantages of PTMs in sequentially arriving tasks. By decomposing the CL objective into hierarchical components, the unified approach optimizes task-specific and task-shared knowledge via mainstream PET techniques. This framework is evaluated across various CL scenarios, demonstrating superior performance over strong baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper studies how to improve the way computers learn new things without forgetting what they already know. It uses special models that were trained on a lot of data beforehand. The goal is to make these models better at learning new tasks without losing their knowledge from earlier tasks. The researchers developed a new approach called Hierarchical Decomposition PET, which helps the model learn in a way that’s more effective and efficient. They tested this approach with different methods and found that it outperformed other existing methods. |
Keywords
* Artificial intelligence * Continual learning * Parameter efficient
