Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AttnGAN: Fine-Grained Text to Image Generation with Attentional
  Generative Adversarial Networks   In this paper, we propose an Attentional Generative Adversarial Network
(AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained
text-to-image generation. With a novel attentional generative network, the
AttnGAN can synthesize fine-grained details at different subregions of the
image by paying attentions to the relevant words in the natural language
description. In addition, a deep attentional multimodal similarity model is
proposed to compute a fine-grained image-text matching loss for training the
generator. The proposed AttnGAN significantly outperforms the previous state of
the art, boosting the best reported inception score by 14.14% on the CUB
dataset and 170.25% on the more challenging COCO dataset. A detailed analysis
is also performed by visualizing the attention layers of the AttnGAN. It for
the first time shows that the layered attentional GAN is able to automatically
select the condition at the word level for generating different parts of the
image.

33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph