Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Multiple Self-Supervised Tasks Improves Model Robustness   Deep networks achieve state-of-the-art performance on computer vision tasks,
yet they fail under adversarial attacks that are imperceptible to humans. In
this paper, we propose a novel defense that can dynamically adapt the input
using the intrinsic structure from multiple self-supervised tasks. By
simultaneously using many self-supervised tasks, our defense avoids
over-fitting the adapted image to one specific self-supervised task and
restores more intrinsic structure in the image compared to a single
self-supervised task approach. Our approach further improves robustness and
clean accuracy significantly compared to the state-of-the-art single task
self-supervised defense. Our work is the first to connect multiple
self-supervised tasks to robustness, and suggests that we can achieve better
robustness with more intrinsic signal from visual data.

16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys