# D\\'ej\\\`a Vu Memorization in Vision-Language Models Source: [D\\'ej\\\`a Vu Memorization in Vision-Language Models]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:33:53.801Z Authors: Bargav Jayaraman, Chuan Guo, Kamalika Chaudhuri Published: 2024-02-03T09:55:35.000Z ## Abstract Vision-Language Models (VLMs) have emerged as the state-of-the-art representation learning solution, with myriads of downstream applications such as image classification, retrieval and generation. A natural question is whether these models memorize their training data, which also has implications for generalization. We propose a new method for measuring memorization in VLMs, which we call d\\'ej\\\`a vu memorization. For VLMs trained on image-caption pairs, we show that the model indeed retains information about individual objects in the training images beyond what can be inferred from correlations or the image caption. We evaluate d\\'ej\\\`a vu memorization at both sample and population level, and show that it is significant for OpenCLIP trained on as many as 50M image-caption pairs. Finally, we show that text randomization considerably mitigates memorization while only moderately impacting the model's downstream task performance. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()