arXiv:2606.22220v2 Announce Type: replace-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, the first metric designed to quantify memorization in multi-modal contrastive learning. Throug
The proliferation of multi-modal AI models necessitates new methods for evaluating their robustness and potential vulnerabilities, which this research addresses.
Understanding and mitigating memorization is critical for building trustworthy and generalizable AI systems, particularly in sensitive applications and for large-scale deployments.
The introduction of MultiMem provides the first specific metric for quantifying memorization in multi-modal contrastive learning, enabling more rigorous evaluation and development practices.
- · AI researchers and developers
- · High-stakes AI applications
- · Responsible AI frameworks
- · Models with poor generalization
- · AI systems prone to memorized noise
Improved methods for training and fine-tuning multi-modal AI models will emerge.
Reduced risk of AI biases and vulnerabilities arising from memorization will enhance public trust in AI.
The ability to quantify and control memorization could lead to more efficient and specialized AI architectures.
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Read at arXiv cs.AI