This repository contains the ImageNet-P dataset from Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. If you find this useful in your research, please consider citing: @article{hendrycks2019robustness, title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations}, author={Hendrycks, Dan and Dietterich, Thomas}, journal={Proceedings of the International Conference on Learning Representations}, year={2019}
Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-lea...
Convolutional neural networks (CNNs) lack robustness to test image corruptions that are not seen dur...
Convolutional neural networks (CNNs) lack robustness to test image corruptions that are not seen dur...
This repository contains the Tiny ImageNet-C and Tiny ImageNet-P dataset from Benchmarking Neural Ne...
This repository contains the Tiny ImageNet-C and Tiny ImageNet-P dataset from Benchmarking Neural Ne...
This repository contains the Tiny ImageNet-C and Tiny ImageNet-P dataset from Benchmarking Neural Ne...
This repository contains the CIFAR-100-C dataset from Benchmarking Neural Network Robustness to Comm...
This repository contains the CIFAR-10-C and CIFAR-10-P dataset from Benchmarking Neural Network Robu...
We introduce several new datasets namely ImageNet-A/O and ImageNet-R as well as a synthetic environm...
Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-lea...
Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-lea...
This repository contains the ImageNet-C dataset from Benchmarking Neural Network Robustness to Commo...
Upload of the corrupted version of Tiny ImageNet (also known as ImageNet-200) with fixed frost-corru...
This dataset contains examples of semantically-perturbed images, for NeurIPS 2020 submission #4915. ...
Classification performance based on ImageNet is the de-facto standard metric for CNN development. In...
Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-lea...
Convolutional neural networks (CNNs) lack robustness to test image corruptions that are not seen dur...
Convolutional neural networks (CNNs) lack robustness to test image corruptions that are not seen dur...
This repository contains the Tiny ImageNet-C and Tiny ImageNet-P dataset from Benchmarking Neural Ne...
This repository contains the Tiny ImageNet-C and Tiny ImageNet-P dataset from Benchmarking Neural Ne...
This repository contains the Tiny ImageNet-C and Tiny ImageNet-P dataset from Benchmarking Neural Ne...
This repository contains the CIFAR-100-C dataset from Benchmarking Neural Network Robustness to Comm...
This repository contains the CIFAR-10-C and CIFAR-10-P dataset from Benchmarking Neural Network Robu...
We introduce several new datasets namely ImageNet-A/O and ImageNet-R as well as a synthetic environm...
Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-lea...
Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-lea...
This repository contains the ImageNet-C dataset from Benchmarking Neural Network Robustness to Commo...
Upload of the corrupted version of Tiny ImageNet (also known as ImageNet-200) with fixed frost-corru...
This dataset contains examples of semantically-perturbed images, for NeurIPS 2020 submission #4915. ...
Classification performance based on ImageNet is the de-facto standard metric for CNN development. In...
Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-lea...
Convolutional neural networks (CNNs) lack robustness to test image corruptions that are not seen dur...
Convolutional neural networks (CNNs) lack robustness to test image corruptions that are not seen dur...