Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Studying Invariances of Trained Convolutional Neural Networks   Convolutional Neural Networks (CNNs) define an exceptionally powerful class
of models for image classification, but the theoretical background and the
understanding of how invariances to certain transformations are learned is
limited. In a large scale screening with images modified by different affine
and nonaffine transformations of varying magnitude, we analyzed the behavior of
the CNN architectures AlexNet and ResNet. If the magnitude of different
transformations does not exceed a class- and transformation dependent
threshold, both architectures show invariant behavior. In this work we
furthermore introduce a new learnable module, the Invariant Transformer Net,
which enables us to learn differentiable parameters for a set of affine
transformations. This allows us to extract the space of transformations to
which the CNN is invariant and its class prediction robust.

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The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg