Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PaRoT: A Practical Framework for Robust Deep Neural Network Training   Deep Neural Networks (DNNs) are finding important applications in
safety-critical systems such as Autonomous Vehicles (AVs), where perceiving the
environment correctly and robustly is necessary for safe operation. Raising
unique challenges for assurance due to their black-box nature, DNNs pose a
fundamental problem for regulatory acceptance of these types of systems. Robust
training --- training to minimize excessive sensitivity to small changes in
input --- has emerged as one promising technique to address this challenge.
However, existing robust training tools are inconvenient to use or apply to
existing codebases and models: they typically only support a small subset of
model elements and require users to extensively rewrite the training code. In
this paper we introduce a novel framework, PaRoT, developed on the popular
TensorFlow platform, that greatly reduces the barrier to entry. Our framework
enables robust training to be performed on arbitrary DNNs without any rewrites
to the model. We demonstrate that our framework's performance is comparable to
prior art, and exemplify its ease of use on off-the-shelf, trained models and
its testing capabilities on a real-world industrial application: a traffic
light detection network.

37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat