text: 444,49,1151515352121004032,68538286,Dan Hendrycks,"Natural Adversarial Examples are real-world and unmodified examples which cause classifiers to be consistently confused. The new dataset has 7,500 images, which we personally labeled over several months. Paper: <LINK> Dataset and code: <LINK> <LINK> @neuroecology @rbhar90 From the introduction, @goodfellow_ian et al. define adversarial examples as “inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake.” @gwern @neuroecology @rbhar90 Yes, this is hard example mining, just as adversarial l_p perturbations are perturbations mined from the l_p ball.",https://arxiv.org/abs/1907.07174,"We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to create datasets with limited spurious cues. Our datasets' real-world, unmodified examples transfer to various unseen models reliably, demonstrating that computer vision models have shared weaknesses. The first dataset is called ImageNet-A and is like the ImageNet test set, but it is far more challenging for existing models. We also curate an adversarial out-of-distribution detection dataset called ImageNet-O, which is the first out-of-distribution detection dataset created for ImageNet models. On ImageNet-A a DenseNet-121 obtains around 2% accuracy, an accuracy drop of approximately 90%, and its out-of-distribution detection performance on ImageNet-O is near random chance levels. We find that existing data augmentation techniques hardly boost performance, and using other public training datasets provides improvements that are limited. However, we find that improvements to computer vision architectures provide a promising path towards robust models. ",Natural Adversarial Examples,3,"['Natural Adversarial Examples are real-world and unmodified examples which cause classifiers to be consistently confused. The new dataset has 7,500 images, which we personally labeled over several months.\nPaper: <LINK>\nDataset and code: <LINK> <LINK>', '@neuroecology @rbhar90 From the introduction, @goodfellow_ian et al. define adversarial examples as “inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake.”', '@gwern @neuroecology @rbhar90 Yes, this is hard example mining, just as adversarial l_p perturbations are perturbations mined from the l_p ball.']",19,07,612