MultiMem: Measuring and Mitigating Memorization in Multi-Modal Contrastive Learning
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. Through our systematic analysis, we demonstrate that cross-modal semantic misalignment has the strongest influence on memorization, with text being the dominant ...
| Authors | Wenhao Wang, Franziska Boenisch, Michael Backes et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 June 2026 |
| Last updated at source | 23 June 2026 |
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