In recent years, the recognition of free-hand sketches has remained a popular
task. However, in some special fields such as the military field, free-hand
sketches are difficult to sample on a large scale. Common data augmentation and
image generation techniques are difficult to produce images with various
free-hand sketching styles. Therefore, the recognition and segmentation tasks
in related fields are limited. In this paper, we propose a novel adversarial
generative network that can accurately generate realistic free-hand sketches
with various styles. We explore the performance of the model, including using
styles randomly sampled from a prior normal distribution to generate images
with various free-hand sketching styles, disentangling the painters' styles
from known free-hand sketches to generate images with specific styles, and
generating images of unknown classes that are not in the training set. We
further demonstrate with qualitative and quantitative evaluations our
advantages in visual quality, content accuracy, and style imitation on
SketchIME.

在本文中，我们提出了一种新颖的对抗生成网络，可以准确地生成具有各种风格的逼真手绘草图，并通过定性和定量评估展示了我们在视觉质量、内容准确性和风格模仿方面的优势，其中应用于 SketchIME。

内容条件下的风格化手绘生成

Content-Conditioned Generation of Stylized Free hand Sketches

In this paper, we focus on the task of generating a pun sentence given a pair
of word senses. A major challenge for pun generation is the lack of large-scale
pun corpus to guide the supervised learning. To remedy this, we propose an
adversarial generative network for pun generation (Pun-GAN), which does not
require any pun corpus. It consists of a generator to produce pun sentences,
and a discriminator to distinguish between the generated pun sentences and the
real sentences with specific word senses. The output of the discriminator is
then used as a reward to train the generator via reinforcement learning,
encouraging it to produce pun sentences that can support two word senses
simultaneously. Experiments show that the proposed Pun-GAN can generate
sentences that are more ambiguous and diverse in both automatic and human
evaluation.

本文介绍了一种无需预先收集大量双关语语料库的生成双关语句子的方法 ——Pun-GAN，该方法采用对抗生成网络和强化学习，并在自动和人类评估中取得了更大的歧义性和多样性效果。