Efficient Exploration of Image Classifier Failures with Bayesian Optimization and Text-to-Image Models

arXiv:2405.02332v1 Announce Type: new
Abstract: Image classifiers should be used with caution in the real world. Performance evaluated on a validation set may not reflect performance in the real world. In particular, classifiers may perform well for conditions that are frequently encountered during training, but poorly for other infrequent conditions. In this study, we hypothesize that recent advances in text-to-image generative models make them valuable for benchmarking computer vision models such as image classifiers: they can generate images conditioned by …

abstract arxiv bayesian caution classifier classifiers cs.ai cs.cv exploration image optimization performance set text text-to-image training type validation world