# Adversarial Training For Sketch Retrieval

arXiv: https://arxiv.org/abs/1607.02748 · HTML: https://mlsanity.com/paper/1607.02748/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2016.

## Built on by (most cited first, 4 of 4)

- [Differentiable Drawing and Sketching](https://mlsanity.com/paper/2103.16194/) (2021)
- [Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey](https://mlsanity.com/paper/2111.13282/) (2021)
- [Zero-shot sketch-based remote sensing image retrieval based on multi-level and attention-guided tokenization](https://mlsanity.com/paper/2402.02141/) (2024)
- [ViSketch-GPT: Collaborative Multi-Scale Feature Extraction for Sketch Recognition and Generation](https://mlsanity.com/paper/2503.22374/) (2025)

## Abstract (arXiv)

Generative Adversarial Networks (GAN) are able to learn excellent representations for unlabelled data which can be applied to image generation and scene classification. Representations learned by GANs have not yet been applied to retrieval. In this paper, we show that the representations learned by GANs can indeed be used for retrieval. We consider heritage documents that contain unlabelled Merchant Marks, sketch-like symbols that are similar to hieroglyphs. We introduce a novel GAN architecture with design features that make it suitable for sketch retrieval. The performance of this sketch-GAN is compared to a modified version of the original GAN architecture with respect to simple invariance properties. Experiments suggest that sketch-GANs learn representations that are suitable for retrieval and which also have increased stability to rotation, scale and translation compared to the standard GAN architecture.