Déjà Vu Memorization in Vision-Language Models
Bargav Jayaraman Chuan Guo Kamalika Chaudhuri
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.