ImageNet-P
Introduced by Hendrycks et al. in Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
ImageNet-P consists of noise, blur, weather, and digital distortions. The dataset has validation perturbations; has difficulty levels; has CIFAR-10, Tiny ImageNet, ImageNet 64 × 64, standard, and Inception-sized editions; and has been designed for benchmarking not training networks. ImageNet-P departs from ImageNet-C by having perturbation sequences generated from each ImageNet validation image. Each sequence contains more than 30 frames, so to counteract an increase in dataset size and evaluation time only 10 common perturbations are used.
Source: Benchmarking Neural Network Robustness to Common Corruptions and PerturbationsBenchmarks
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ImageNet-P
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SqueezeNet + Simple Bypass
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Papers
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