Research from Renmin University: Caution Advised in Data Augmentation for Contrastive Learning

新智元

Published inAI News · 1 min read · Nov 16, 2023

The latest research from Renmin University warns that caution is needed in data augmentation for contrastive learning. Strongly aligned positive samples may harm the generalization ability of contrastive learning. While stronger data augmentation can enhance downstream task performance, alignment performance decreases. The study reveals the mechanisms by which data augmentation affects contrastive learning and proposes seeking better data augmentation strategies from an information-theoretic and spectral perspective.

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Research from Renmin University: Caution Advised in Data Augmentation for Contrastive Learning

新智元

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Tencent Releases New Patent on Training Large Language Models to Enhance Generalization and Accuracy

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MIT and Google Launch StableRep Technology to Train Efficient AI Models Using AI-Generated Images