Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Context Encoders: Feature Learning by Inpainting   We present an unsupervised visual feature learning algorithm driven by
context-based pixel prediction. By analogy with auto-encoders, we propose
Context Encoders -- a convolutional neural network trained to generate the
contents of an arbitrary image region conditioned on its surroundings. In order
to succeed at this task, context encoders need to both understand the content
of the entire image, as well as produce a plausible hypothesis for the missing
part(s). When training context encoders, we have experimented with both a
standard pixel-wise reconstruction loss, as well as a reconstruction plus an
adversarial loss. The latter produces much sharper results because it can
better handle multiple modes in the output. We found that a context encoder
learns a representation that captures not just appearance but also the
semantics of visual structures. We quantitatively demonstrate the effectiveness
of our learned features for CNN pre-training on classification, detection, and
segmentation tasks. Furthermore, context encoders can be used for semantic
inpainting tasks, either stand-alone or as initialization for non-parametric
methods.

14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph