Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Be Like Water: Robustness to Extraneous Variables Via Adaptive Feature
  Normalization   Extraneous variables are variables that are irrelevant for a certain task,
but heavily affect the distribution of the available data. In this work, we
show that the presence of such variables can degrade the performance of
deep-learning models. We study three datasets where there is a strong influence
of known extraneous variables: classification of upper-body movements in stroke
patients, annotation of surgical activities, and recognition of corrupted
images. Models trained with batch normalization learn features that are highly
dependent on the extraneous variables. In batch normalization, the statistics
used to normalize the features are learned from the training set and fixed at
test time, which produces a mismatch in the presence of varying extraneous
variables. We demonstrate that estimating the feature statistics adaptively
during inference, as in instance normalization, addresses this issue, producing
normalized features that are more robust to changes in the extraneous
variables. This results in a significant gain in performance for different
network architectures and choices of feature statistics.

3
The categories of this paper are: physics
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
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The categories of this paper are: eess
30
The categories of this paper are: q-alg