We study the effectiveness of data-balancing for mitigating biases in
contrastive language-image pretraining (CLIP), identifying areas of strength
and limitation. First, we reaffirm prior conclusions that CLIP models can
inadvertently absorb societal stereotypes. To counter this, we present a novel
algorithm, called Multi-Modal Moment Matching (M4), designed to reduce both
representation and association biases (i.e. in first- and second-order
statistics) 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 and
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.

探讨使用数据平衡来减轻对比语言 - 图像预训练（CLIP）中的偏见的有效性，提出了一种名为 Multi-Modal Moment Matching（M4）的新算法，用于减少多模态数据中的表示和关联偏见，并对 CLIP 对偏见的学习和遗忘的动态性进行了分析。