Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generative Cooperative Net for Image Generation and Data Augmentation   How to build a good model for image generation given an abstract concept is a
fundamental problem in computer vision. In this paper, we explore a generative
model for the task of generating unseen images with desired features. We
propose the Generative Cooperative Net (GCN) for image generation. The idea is
similar to generative adversarial networks except that the generators and
discriminators are trained to work accordingly. Our experiments on hand-written
digit generation and facial expression generation show that GCN's two
cooperative counterparts (the generator and the classifier) can work together
nicely and achieve promising results. We also discovered a usage of such
generative model as an data-augmentation tool. Our experiment of applying this
method on a recognition task shows that it is very effective comparing to other
existing methods. It is easy to set up and could help generate a very large
synthesized dataset.

37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
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The categories of this paper are: eess
14
The categories of this paper are: math-ph