Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Invariance-inducing regularization using worst-case transformations
  suffices to boost accuracy and spatial robustness   This work provides theoretical and empirical evidence that
invariance-inducing regularizers can increase predictive accuracy for
worst-case spatial transformations (spatial robustness). Evaluated on these
adversarially transformed examples, we demonstrate that adding regularization
on top of standard or adversarial training reduces the relative error by 20%
for CIFAR10 without increasing the computational cost. This outperforms
handcrafted networks that were explicitly designed to be spatial-equivariant.
Furthermore, we observe for SVHN, known to have inherent variance in
orientation, that robust training also improves standard accuracy on the test
set. We prove that this no-trade-off phenomenon holds for adversarial examples
from transformation groups in the infinite data limit.

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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: chem-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: math
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg