Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Transformation Learning via Convex Relaxations   Our goal is to extract meaningful transformations from raw images, such as
varying the thickness of lines in handwriting or the lighting in a portrait. We
propose an unsupervised approach to learn such transformations by attempting to
reconstruct an image from a linear combination of transformations of its
nearest neighbors. On handwritten digits and celebrity portraits, we show that
even with linear transformations, our method generates visually high-quality
modified images. Moreover, since our method is semiparametric and does not
model the data distribution, the learned transformations extrapolate off the
training data and can be applied to new types of images.

29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
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The categories of this paper are: econ
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg