Can a user create a deep generative model by sketching a single example?
Traditionally, creating a GAN model has required the collection of a
large-scale dataset of exemplars and specialized knowledge in deep learning. In
contrast, sketching is possibly the most universally accessible way to convey a
visual concept. In this work, we present a method, GAN Sketching, for rewriting
GANs with one or more sketches, to make GANs training easier for novice users.
In particular, we change the weights of an original GAN model according to user
sketches. We encourage the model's output to match the user sketches through a
cross-domain adversarial loss. Furthermore, we explore different regularization
methods to preserve the original model's diversity and image quality.
Experiments have shown that our method can mold GANs to match shapes and poses
specified by sketches while maintaining realism and diversity. Finally, we
demonstrate a few applications of the resulting GAN, including latent space
interpolation and image editing.

通过 GAN Sketching 方法实现 GAN 模型的简化，即利用一个或若干草图改变 GAN 模型的权重，鼓励模型输出与用户草图匹配，同时保留原始模型的多样性和图像质量，实现了潜空间插值和图像编辑。

手绘你自己的生成对抗网络

Sketch Your Own GAN

Unsupervised meta-learning approaches rely on synthetic meta-tasks that are
created using techniques such as random selection, clustering and/or
augmentation. Unfortunately, clustering and augmentation are domain-dependent,
and thus they require either manual tweaking or expensive learning. In this
work, we describe an approach that generates meta-tasks using generative
models. A critical component is a novel approach of sampling from the latent
space that generates objects grouped into synthetic classes forming the
training and validation data of a meta-task. We find that the proposed
approach, LAtent Space Interpolation Unsupervised Meta-learning (LASIUM),
outperforms or is competitive with current unsupervised learning baselines on
few-shot classification tasks on the most widely used benchmark datasets. In
addition, the approach promises to be applicable without manual tweaking over a
wider range of domains than previous approaches.

本文提出一种基于生成模型的元任务生成方法，称为 LAtent Space Interpolation Unsupervised Meta-learning (LASIUM)，通过从潜在空间中生成分组成群的对象，构建元任务的训练和验证数据，应用于少样本分类任务中的实验表明其性能优于或与当前无监督学习基线相竞争，并且该方法不需要手工调整，扩展性高。