Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Guiding GANs: How to control non-conditional pre-trained GANs for
  conditional image generation   Generative Adversarial Networks (GANs) are an arrange of two neural networks
-- the generator and the discriminator -- that are jointly trained to generate
artificial data, such as images, from random inputs. The quality of these
generated images has recently reached such levels that can often lead both
machines and humans into mistaking fake for real examples. However, the process
performed by the generator of the GAN has some limitations when we want to
condition the network to generate images from subcategories of a specific
class. Some recent approaches tackle this \textit{conditional generation} by
introducing extra information prior to the training process, such as image
semantic segmentation or textual descriptions. While successful, these
techniques still require defining beforehand the desired subcategories and
collecting large labeled image datasets representing them to train the GAN from
scratch. In this paper we present a novel and alternative method for guiding
generic non-conditional GANs to behave as conditional GANs. Instead of
re-training the GAN, our approach adds into the mix an encoder network to
generate the high-dimensional random input vectors that are fed to the
generator network of a non-conditional GAN to make it generate images from a
specific subcategory. In our experiments, when compared to training a
conditional GAN from scratch, our guided GAN is able to generate artificial
images of perceived quality comparable to that of non-conditional GANs after
training the encoder on just a few hundreds of images, which substantially
accelerates the process and enables adding new subcategories seamlessly.

22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg