Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving the affordability of robustness training for DNNs   Projected Gradient Descent (PGD) based adversarial training has become one of
the most prominent methods for building robust deep neural network models.
However, the computational complexity associated with this approach, due to the
maximization of the loss function when finding adversaries, is a longstanding
problem and may be prohibitive when using larger and more complex models. In
this paper we show that the initial phase of adversarial training is redundant
and can be replaced with natural training which significantly improves the
computational efficiency. We demonstrate that this efficiency gain can be
achieved without any loss in accuracy on natural and adversarial test samples.
We support our argument with insights on the nature of the adversaries and
their relative strength during the training process. We show that our proposed
method can reduce the training time by a factor of up to 2.5 with comparable or
better model test accuracy and generalization on various strengths of
adversarial attacks.

4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math