MultiMem: Measuring and Mitigating Memorization in Multi-Modal Contrastive Learninga

📰 ArXiv cs.AI

Learn to measure and mitigate memorization in multi-modal contrastive learning using MultiMem, improving model generalization and robustness

advanced Published 23 Jun 2026

Action Steps

Implement MultiMem to measure memorization in multi-modal contrastive learning models
Analyze the results to identify areas where memorization is harming model generalization
Apply mitigation techniques to reduce memorization and improve model robustness
Evaluate the effectiveness of mitigation techniques using metrics such as accuracy and robustness
Refine and iterate on the mitigation strategies to optimize model performance

Who Needs to Know This

Researchers and engineers working on multi-modal machine learning models can benefit from this knowledge to improve their model's performance and robustness

Key Insight

💡 Memorization in multi-modal contrastive learning can harm model generalization, but measuring and mitigating it can improve model performance and robustness

Key Takeaways

Learn to measure and mitigate memorization in multi-modal contrastive learning using MultiMem, improving model generalization and robustness

Full Article

Title: MultiMem: Measuring and Mitigating Memorization in Multi-Modal Contrastive Learninga

Abstract:
arXiv:2606.22220v1 Announce Type: cross Abstract: Memorization in machine learning models enables high performance on rare in-distribution samples by capturing their atypical patterns. However, it also causes harmful retention of noise and outliers, degrading generalization. While memorization has been extensively studied in both supervised and self-supervised learning in the vision domain, it remains unexplored in multi-modal contrastive learning. We address this gap by introducing MultiMem, th

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