Summary of Continual Learning in the Presence Of Repetition, by Hamed Hemati et al.
Continual Learning in the Presence of Repetition
by Hamed Hemati, Lorenzo Pellegrini, Xiaotian Duan, Zixuan Zhao, Fangfang Xia, Marc Masana, Benedikt Tscheschner, Eduardo Veas, Yuxiang Zheng, Shiji Zhao, Shao-Yuan Li, Sheng-Jun Huang, Vincenzo Lomonaco, Gido M. van de Ven
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a summary of the Continual Learning Vision (CLVision) challenge at CVPR 2023, focusing on repetition in class-incremental learning. The challenge aims to effectively exploit repetition in the data stream to learn continually. Three finalist teams proposed solutions that employ ensemble-based approaches, leveraging multiple versions of similar modules trained on different but overlapping subsets of classes. Experimental results highlight the effectiveness of these strategies. This report underscores the transformative potential of considering repetition in the data stream for innovative strategy design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a big challenge in machine learning called Continual Learning. The goal is to train models that can learn from new information and adapt to changing situations. The authors looked at how we can use something called “repetition” in the data, which means seeing things or tasks again after they first appeared. They found that some teams did a great job of using this repetition to improve their models. This is important because it could help us make better decisions and solve problems more effectively. |
Keywords
» Artificial intelligence » Continual learning » Machine learning
