Summary of Dealing with Synthetic Data Contamination in Online Continual Learning, by Maorong Wang et al.
Dealing with Synthetic Data Contamination in Online Continual Learning
by Maorong Wang, Nicolas Michel, Jiafeng Mao, Toshihiko Yamasaki
First submitted to arxiv on: 21 Nov 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 investigates the impact of AI-generated images on machine learning research. Specifically, it explores how contaminated datasets with synthetic images affect Online Continual Learning (CL) methods. The authors show that using such datasets can hinder training and propose a method called Entropy Selection with Real-synthetic similarity Maximization (ESRM) to alleviate this issue. Experiments demonstrate that ESRM significantly improves performance when dealing with severe contamination. The proposed solution leverages diffusion-based models, which have achieved remarkable results in generating high-fidelity realistic images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how fake AI-generated pictures might affect machine learning research. Right now, these images are being added to the internet and could make it harder for future researchers to find clean data to train their models. Some earlier studies showed that using datasets with fake images can make training worse. This paper explores this problem specifically in Online Continual Learning (CL) methods. It finds that contaminated datasets do indeed make training harder and proposes a solution called ESRM to fix the issue. |
Keywords
» Artificial intelligence » Continual learning » Diffusion » Machine learning
