# SketchEmbedNet: Learning Novel Concepts by Imitating Drawings

arXiv: https://arxiv.org/abs/2009.04806 · HTML: https://mlsanity.com/paper/2009.04806/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 18 of 18)

- [Learning to generate line drawings that convey geometry and semantics](https://mlsanity.com/paper/2203.12691/) (2022)
- [Picture that Sketch: Photorealistic Image Generation from Abstract Sketches](https://mlsanity.com/paper/2303.11162/) (2023)
- [Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches](https://mlsanity.com/paper/2203.14843/) (2022)
- [Sketch2Saliency: Learning to Detect Salient Objects from Human Drawings](https://mlsanity.com/paper/2303.11502/) (2023)
- [Cloud2Curve: Generation and Vectorization of Parametric Sketches](https://mlsanity.com/paper/2103.15536/) (2021)
- [Emergent Graphical Conventions in a Visual Communication Game](https://mlsanity.com/paper/2111.14210/) (2021)
- [SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction](https://mlsanity.com/paper/2312.03035/) (2023)
- [SketchINR: A First Look into Sketches as Implicit Neural Representations](https://mlsanity.com/paper/2403.09344/) (2024)
- [SketchXAI: A First Look at Explainability for Human Sketches](https://mlsanity.com/paper/2304.11744/) (2023)
- [FlipSketch: Flipping Static Drawings to Text-Guided Sketch Animations](https://mlsanity.com/paper/2411.10818/) (2024)
- [Emergent Communication in Interactive Sketch Question Answering](https://mlsanity.com/paper/2310.15597/) (2023)
- [Do Generalised Classifiers really work on Human Drawn Sketches?](https://mlsanity.com/paper/2407.03893/) (2024)
- [NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation](https://mlsanity.com/paper/2106.13435/) (2021)
- [A Relational Model for One-Shot Classification](https://mlsanity.com/paper/2111.04313/) (2021)
- [Character Generation through Self-Supervised Vectorization](https://mlsanity.com/paper/2208.02012/) (2022)
- [Shape Guided Gradient Voting for Domain Generalization](https://mlsanity.com/paper/2306.10809/) (2023)
- [Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection](https://mlsanity.com/paper/2507.07994/) (2025)
- [Annotation-Free Human Sketch Quality Assessment](https://mlsanity.com/paper/2507.20548/) (2025)

## Abstract (arXiv)

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.