Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantifying the Preferential Direction of the Model Gradient in
  Adversarial Training With Projected Gradient Descent   Adversarial training, especially projected gradient descent (PGD), has been a
successful approach for improving robustness against adversarial attacks. After
adversarial training, gradients of models with respect to their inputs have a
preferential direction. However, the direction of alignment is not
mathematically well established, making it difficult to evaluate
quantitatively. We propose a novel definition of this direction as the
direction of the vector pointing toward the closest point of the support of the
closest inaccurate class in decision space. To evaluate the alignment with this
direction after adversarial training, we apply a metric that uses generative
adversarial networks to produce the smallest residual needed to change the
class present in the image. We show that PGD-trained models have a higher
alignment than the baseline according to our definition, that our metric
presents higher alignment values than a competing metric formulation, and that
enforcing this alignment increases the robustness of models.

24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con