Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-Supervised Learning with Context-Conditional Generative Adversarial
  Networks   We introduce a simple semi-supervised learning approach for images based on
in-painting using an adversarial loss. Images with random patches removed are
presented to a generator whose task is to fill in the hole, based on the
surrounding pixels. The in-painted images are then presented to a discriminator
network that judges if they are real (unaltered training images) or not. This
task acts as a regularizer for standard supervised training of the
discriminator. Using our approach we are able to directly train large VGG-style
networks in a semi-supervised fashion. We evaluate on STL-10 and PASCAL
datasets, where our approach obtains performance comparable or superior to
existing methods.

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The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
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The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
18
The categories of this paper are: econ