Summary of Continual Learning with Pre-trained Models: a Survey, by Da-wei Zhou et al.
Continual Learning with Pre-Trained Models: A Survey
by Da-Wei Zhou, Hai-Long Sun, Jingyi Ning, Han-Jia Ye, De-Chuan Zhan
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This research paper presents a comprehensive survey of the latest advancements in Pre-trained Model (PTM)-based Continual Learning (CL), which aims to achieve robust and efficient learning from streaming data. The authors categorize existing methodologies into three distinct groups, providing a comparative analysis of their similarities, differences, and respective advantages and disadvantages. They also offer an empirical study contrasting various state-of-the-art methods to highlight concerns regarding fairness in comparisons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make computers learn new things from the data they receive over time. This is important because we often get new information that changes what we already know, and we want our computer systems to remember what they learned before. The researchers looked at ways to use pre-trained models (like those used in image recognition) to help with this learning process. They grouped different approaches into three types and compared them to find the best methods. |
Keywords
* Artificial intelligence * Continual learning
