  Sketch drawings capture the salient information of visual concepts. Previous
work has shown that neural networks are capable of producing sketches of
natural objects drawn from a small number of classes. While earlier approaches
focus on generation quality or retrieval, we explore properties of image
representations learned by training a model to produce sketches of images. We
show that this generative, class-agnostic model produces informative embeddings
of images from novel examples, classes, and even novel datasets in a few-shot
setting. Additionally, we find that these learned representations exhibit
interesting structure and compositionality.
