# Conditional GANs For Painting Generation

arXiv: https://arxiv.org/abs/1903.06259 · HTML: https://mlsanity.com/paper/1903.06259/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Controllable Data Generation by Deep Learning: A Review](https://mlsanity.com/paper/2207.09542/) (2022)
- [AI Art Neural Constellation: Revealing the Collective and Contrastive State of AI-Generated and Human Art](https://mlsanity.com/paper/2402.02453/) (2024)
- [st-DTPM: Spatial-Temporal Guided Diffusion Transformer Probabilistic Model for Delayed Scan PET Image Prediction](https://mlsanity.com/paper/2410.22732/) (2024)

## Abstract (arXiv)

We examined the use of modern Generative Adversarial Nets to generate novel images of oil paintings using the Painter By Numbers dataset. We implemented Spectral Normalization GAN (SN-GAN) and Spectral Normalization GAN with Gradient Penalty, and compared their outputs to a Deep Convolutional GAN. Visually, and quantitatively according to the Sliced Wasserstein Distance metric, we determined that the SN-GAN produced paintings that were most comparable to our training dataset. We then performed a series of experiments to add supervised conditioning to SN-GAN, the culmination of which is what we believe to be a novel architecture that can generate face paintings with user-specified characteristics.