Variation of Gender Biases in Visual Recognition Models Before and After Finetuning
This addresses bias mitigation in computer vision systems, highlighting risks in fine-tuning practices, though it is incremental as it builds on existing bias measurement frameworks.
The study measured how gender biases in visual recognition models change before and after fine-tuning, finding that supervised models like those trained on ImageNet-21k retain pretraining biases more than self-supervised models, and larger fine-tuning datasets can introduce new biases.
We introduce a framework to measure how biases change before and after fine-tuning a large scale visual recognition model for a downstream task. Deep learning models trained on increasing amounts of data are known to encode societal biases. Many computer vision systems today rely on models typically pretrained on large scale datasets. While bias mitigation techniques have been developed for tuning models for downstream tasks, it is currently unclear what are the effects of biases already encoded in a pretrained model. Our framework incorporates sets of canonical images representing individual and pairs of concepts to highlight changes in biases for an array of off-the-shelf pretrained models across model sizes, dataset sizes, and training objectives. Through our analyses, we find that (1) supervised models trained on datasets such as ImageNet-21k are more likely to retain their pretraining biases regardless of the target dataset compared to self-supervised models. We also find that (2) models finetuned on larger scale datasets are more likely to introduce new biased associations. Our results also suggest that (3) biases can transfer to finetuned models and the finetuning objective and dataset can impact the extent of transferred biases.