Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Natural Adversarial Examples   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.

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The categories of this paper are: hep-th
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The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
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The categories of this paper are: gr-qc
3
The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
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The categories of this paper are: econ
25
The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom