CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?

Published: 16 Jan 2024, Last Modified: 07 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: contrastive language image pretraining, multimodal learning, fairness, bias, data balancing
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TL;DR: We investigate the effectiveness of data-balancing for mitigating biases in contrastive language-image pretraining (CLIP) models, identifying areas of strength and limitation.
Abstract: We study data-balancing for mitigating biases in contrastive language-image pretraining (CLIP), identifying areas of strength and limitation. First, we reaffirm prior conclusions that CLIP can inadvertently absorb stereotypes. To counter this, we present a novel algorithm, called Multi-Modal Moment Matching (M4), designed to reduce both representation and association biases in multimodal data. We use M4 to conduct an in-depth analysis taking into account various factors, such as the model, representation, and data size. Our study also explores the dynamic nature of how CLIP learns/unlearns biases. In particular, we find that fine-tuning is effective in countering representation biases, though its impact diminishes for association biases. Also, data balancing has a mixed impact on quality: it tends to improve classification but can hurt retrieval. Interestingly, data and architectural improvements seem to mitigate the negative impact of data balancing on performance; e.g. applying M4 to SigLIP-B/16 with data quality filters improves COCO image-to-text retrieval @5 from 86% (without data balancing) to 87% and ImageNet 0-shot classification from 77% to 77.5%! Finally, we conclude with recommendations for improving the efficacy of data balancing in multimodal systems.
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Primary Area: societal considerations including fairness, safety, privacy
Submission Number: 4102