Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On visual self-supervision and its effect on model robustness   Recent self-supervision methods have found success in learning feature
representations that could rival ones from full supervision, and have been
shown to be beneficial to the model in several ways: for example improving
models robustness and out-of-distribution detection. In our paper, we conduct
an empirical study to understand more precisely in what way can self-supervised
learning - as a pre-training technique or part of adversarial training -
affects model robustness to $l_2$ and $l_{\infty}$ adversarial perturbations
and natural image corruptions. Self-supervision can indeed improve model
robustness, however it turns out the devil is in the details. If one simply
adds self-supervision loss in tandem with adversarial training, then one sees
improvement in accuracy of the model when evaluated with adversarial
perturbations smaller or comparable to the value of $\epsilon_{train}$ that the
robust model is trained with. However, if one observes the accuracy for
$\epsilon_{test} \ge \epsilon_{train}$, the model accuracy drops. In fact, the
larger the weight of the supervision loss, the larger the drop in performance,
i.e. harming the robustness of the model. We identify primary ways in which
self-supervision can be added to adversarial training, and observe that using a
self-supervised loss to optimize both network parameters and find adversarial
examples leads to the strongest improvement in model robustness, as this can be
viewed as a form of ensemble adversarial training. Although self-supervised
pre-training yields benefits in improving adversarial training as compared to
random weight initialization, we observe no benefit in model robustness or
accuracy if self-supervision is incorporated into adversarial training.

16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc