Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: REST: Performance Improvement of a Black Box Model via RL-based Spatial
  Transformation   In recent years, deep neural networks (DNN) have become a highly active area
of research, and shown remarkable achievements on a variety of computer vision
tasks. DNNs, however, are known to often make overconfident yet incorrect
predictions on out-of-distribution samples, which can be a major obstacle to
real-world deployments because the training dataset is always limited compared
to diverse real-world samples. Thus, it is fundamental to provide guarantees of
robustness to the distribution shift between training and test time when we
construct DNN models in practice. Moreover, in many cases, the deep learning
models are deployed as black boxes and the performance has been already
optimized for a training dataset, thus changing the black box itself can lead
to performance degradation. We here study the robustness to the geometric
transformations in a specific condition where the black-box image classifier is
given. We propose an additional learner, \emph{REinforcement Spatial Transform
learner (REST)}, that transforms the warped input data into samples regarded as
in-distribution by the black-box models. Our work aims to improve the
robustness by adding a REST module in front of any black boxes and training
only the REST module without retraining the original black box model in an
end-to-end manner, i.e. we try to convert the real-world data into training
distribution which the performance of the black-box model is best suited for.
We use a confidence score that is obtained from the black-box model to
determine whether the transformed input is drawn from in-distribution. We
empirically show that our method has an advantage in generalization to
geometric transformations and sample efficiency.

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The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an