Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Robustness and Transferability of Convolutional Neural Networks   Modern deep convolutional networks (CNNs) are often criticized for not
generalizing under distributional shifts. However, several recent breakthroughs
in transfer learning suggest that these networks can cope with severe
distribution shifts and successfully adapt to new tasks from a few training
examples. In this work we study the interplay between out-of-distribution and
transfer performance of modern image classification CNNs for the first time and
investigate the impact of the pre-training data size, the model scale, and the
data preprocessing pipeline. We find that increasing both the training set and
model sizes significantly improve the distributional shift robustness.
Furthermore, we show that, perhaps surprisingly, simple changes in the
preprocessing such as modifying the image resolution can significantly mitigate
robustness issues in some cases. Finally, we outline the shortcomings of
existing robustness evaluation datasets and introduce a synthetic dataset
SI-Score we use for a systematic analysis across factors of variation common in
visual data such as object size and position.

9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex