# Adversarial Open Domain Adaption Framework (AODA): Sketch-to-Photo Synthesis

arXiv: https://arxiv.org/abs/2108.04351 · HTML: https://mlsanity.com/paper/2108.04351/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis](https://mlsanity.com/paper/1801.02753/) (2018)
- [SketchyCOCO: Image Generation from Freehand Scene Sketches](https://mlsanity.com/paper/2003.02683/) (2020)
- [Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval](https://mlsanity.com/paper/2002.10310/) (2020)
- [Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation](https://mlsanity.com/paper/1909.11081/) (2019)
- [Open Set Domain Adaptation: Theoretical Bound and Algorithm](https://mlsanity.com/paper/1907.08375/) (2019)
- [Unsupervised Sketch-to-Photo Synthesis](https://mlsanity.com/paper/1909.08313/) (2019)
- [Smart, Sparse Contours to Represent and Edit Images](https://mlsanity.com/paper/1712.08232/) (2017)
- [White-Box Cartoonization Using An Extended GAN Framework](https://mlsanity.com/paper/2107.04551/) (2021)

## Built on by (most cited first, 1 of 1)

- [Frame-Synchronous Hand Gesture Detection by Projected Winding Order](https://mlsanity.com/paper/2609.13269/) (2026)

## Abstract (arXiv)

This paper aims to demonstrate the efficiency of the Adversarial Open Domain Adaption framework for sketch-to-photo synthesis. The unsupervised open domain adaption for generating realistic photos from a hand-drawn sketch is challenging as there is no such sketch of that class for training data. The absence of learning supervision and the huge domain gap between both the freehand drawing and picture domains make it hard. We present an approach that learns both sketch-to-photo and photo-to-sketch generation to synthesise the missing freehand drawings from pictures. Due to the domain gap between synthetic sketches and genuine ones, the generator trained on false drawings may produce unsatisfactory results when dealing with drawings of lacking classes. To address this problem, we offer a simple but effective open-domain sampling and optimization method that tricks the generator into considering false drawings as genuine. Our approach generalises the learnt sketch-to-photo and photo-to-sketch mappings from in-domain input to open-domain categories. On the Scribble and SketchyCOCO datasets, we compared our technique to the most current competing methods. For many types of open-domain drawings, our model outperforms impressive results in synthesising accurate colour, substance, and retaining the structural layout.