Summary of Overfitting in Contrastive Learning?, by Zachary Rabin et al.
Overfitting In Contrastive Learning?
by Zachary Rabin, Jim Davis, Benjamin Lewis, Matthew Scherreik
First submitted to arxiv on: 16 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 This paper investigates the phenomenon of overfitting in unsupervised contrastive learning, a type of machine learning where models are trained to distinguish between similar and dissimilar data points without labels. Despite being well-documented for supervised learning, the occurrence of overfitting in this context has not been thoroughly examined. The authors demonstrate that overfitting can occur and identify its underlying mechanism. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper looks at why machine learning models sometimes become too good at recognizing patterns in training data, making them useless for real-world applications. It focuses on a type of machine learning called unsupervised contrastive learning, where models are trained without labels to understand similarities and differences between data points. The study finds that this phenomenon, known as overfitting, can happen even without labeled data. |
Keywords
» Artificial intelligence » Machine learning » Overfitting » Supervised » Unsupervised
