Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rotating spiders and reflecting dogs: a class conditional approach to
  learning data augmentation distributions   Building invariance to non-meaningful transformations is essential to
building efficient and generalizable machine learning models. In practice, the
most common way to learn invariance is through data augmentation. There has
been recent interest in the development of methods that learn distributions on
augmentation transformations from the training data itself. While such
approaches are beneficial since they are responsive to the data, they ignore
the fact that in many situations the range of transformations to which a model
needs to be invariant changes depending on the particular class input belongs
to. For example, if a model needs to be able to predict whether an image
contains a starfish or a dog, we may want to apply random rotations to starfish
images during training (since these do not have a preferred orientation), but
we would not want to do this to images of dogs. In this work we introduce a
method by which we can learn class conditional distributions on augmentation
transformations. We give a number of examples where our methods learn different
non-meaningful transformations depending on class and further show how our
method can be used as a tool to probe the symmetries intrinsic to a potentially
complex dataset.

24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ