Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring the Vulnerability of Deep Neural Networks: A Study of
  Parameter Corruption   We argue that the vulnerability of model parameters is of crucial value to
the study of model robustness and generalization but little research has been
devoted to understanding this matter. In this work, we propose an indicator to
measure the robustness of neural network parameters by exploiting their
vulnerability via parameter corruption. The proposed indicator describes the
maximum loss variation in the non-trivial worst-case scenario under parameter
corruption. For practical purposes, we give a gradient-based estimation, which
is far more effective than random corruption trials that can hardly induce the
worst accuracy degradation. Equipped with theoretical support and empirical
validation, we are able to systematically investigate the robustness of
different model parameters and reveal vulnerability of deep neural networks
that has been rarely paid attention to before. Moreover, we can enhance the
models accordingly with the proposed adversarial corruption-resistant training,
which not only improves the parameter robustness but also translates into
accuracy elevation.

4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th