Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance

arXiv:2402.19401v1 Announce Type: new
Abstract: While Neural Networks (NNs) have surpassed human accuracy in image classification on ImageNet, they often lack robustness against image corruption, i.e., corruption robustness. Yet such robustness is seemingly effortless for human perception. In this paper, we propose visually-continuous corruption robustness (VCR) -- an extension of corruption robustness to allow assessing it over the wide and continuous range of changes that correspond to the human perceptive quality (i.e., from the original image to the full distortion …

abstract accuracy arxiv classification continuous corruption cs.cv extension human human performance image imagenet networks neural networks nns paper perception performance robustness type