ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object

arXiv:2403.18775v1 Announce Type: cross
Abstract: We establish rigorous benchmarks for visual perception robustness. Synthetic images such as ImageNet-C, ImageNet-9, and Stylized ImageNet provide specific type of evaluation over synthetic corruptions, backgrounds, and textures, yet those robustness benchmarks are restricted in specified variations and have low synthetic quality. In this work, we introduce generative model as a data source for synthesizing hard images that benchmark deep models' robustness. Leveraging diffusion models, we are able to generate images with more diversified backgrounds, …

arxiv benchmarking cs.ai cs.cv cs.lg diffusion imagenet network neural network object robustness synthetic type