Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Synthesizing Informative Training Samples with GAN   Remarkable progress has been achieved in synthesizing photo-realistic images
with generative adversarial neural networks (GANs). Recently, GANs are utilized
as the training sample generator when obtaining or storing real training data
is expensive even infeasible. However, traditional GANs generated images are
not as informative as the real training samples when being used to train deep
neural networks. In this paper, we propose a novel method to synthesize
Informative Training samples with GAN (IT-GAN). Specifically, we freeze a
pre-trained GAN model and learn the informative latent vectors that corresponds
to informative training samples. The synthesized images are required to
preserve information for training deep neural networks rather than visual
reality or fidelity. Experiments verify that the deep neural networks can learn
faster and achieve better performance when being trained with our IT-GAN
generated images. We also show that our method is a promising solution to
dataset condensation problem.

36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol