TY - JOUR
AU - Jegelka, Stefanie
AB - Abstract: Machine learning models have been shown to inherit biases from their training datasets. This can be particularly problematic for vision-language foundation models trained on uncurated datasets scraped from the internet. The biases can be amplified and propagated to downstream applications like zero-shot classifiers and text-to-image generative models. In this study, we propose a general approach for debiasing vision-language foundation models by projecting out biased directions in the text embedding. In particular, we show that debiasing only the text embedding with a calibrated projection matrix suffices to yield robust classifiers and fair generative models. The proposed closed-form solution enables easy integration into large-scale pipelines, and empirical results demonstrate that our approach effectively reduces social bias and spurious correlation in both discriminative and generative vision-language models without the need for additional data or training.
TI - Debiasing Vision-Language Models via Biased Prompts
JF - Computing Research Repository
DO - 10.48550/arxiv.2302.00070
DA - 2023-01-31
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/debiasing-vision-language-models-via-biased-prompts-9rnpE8ORxJ
VL - 2023
IS - 2302
DP - DeepDyve
ER -