Title: Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning. Abstract: Automatically discovering failures in vision models under real-world settings remains an open challenge. This work shows how off-the-shelf, large-scale, image-to-text and text-to-image models, trained on vast amounts of data, can be leveraged to automatically find such failures. In essence, a conditional text-to-image generative model is used to generate large amounts of synthetic, yet realistic, inputs given a ground-truth label. A captioning model is used to describe misclassified inputs. Descriptions are used in turn to generate more inputs, thereby assessing whether specific descriptions induce more failures than expected. As failures are grounded to natural language, we automatically obtain a high-level, human-interpretable explanation of each failure. We use this pipeline to demonstrate that we can effectively interrogate classifiers trained on ImageNet to find specific failure cases and discover spurious correlations. We also show that we can scale the approach to generate adversarial datasets targeting specific classifier architectures. This work demonstrates the utility of large-scale generative models to automatically discover bugs in vision models in an open-ended manner. We also describe a number of limitations and pitfalls related to this approach.