# Simple-PAT

Vision models still fail on seemingly simple scenes, and a fixed dataset cannot reliably estimate worst-case error. Procedural adversarial learning treats an interpretable procedural generator as a controllable data source and uses zeroth-order optimization to search its low-dimensional control parameters for hard instances. On four tasks spanning surface normals, image segmentation and human pose estimation, Simple-PAT lowers worst-case error below uniform training and two robust reweighting baselines against a cross-model black-box adversary with 50M aggregate queries.

Vision models still fail on seemingly simple scenes, and a fixed dataset cannot reliably estimate worst-case error. Procedural adversarial learning treats an interpretable procedural generator as a controllable data source and uses zeroth-order optimization to search its low-dimensional control parameters for hard instances. On four tasks spanning surface normals, image segmentation and human pose estimation, Simple-PAT lowers worst-case error below uniform training and two robust reweighting baselines against a cross-model black-box adversary with 50M aggregate queries.