# DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images

arXiv: https://arxiv.org/abs/1711.05415 · HTML: https://mlsanity.com/paper/1711.05415/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2017.

## Built on by (most cited first, 16 of 16)

- [Disentangled Representation Learning](https://mlsanity.com/paper/2211.11695/) (2022)
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- [Image-to-image Translation via Hierarchical Style Disentanglement](https://mlsanity.com/paper/2103.01456/) (2021)
- [Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images](https://mlsanity.com/paper/2106.07873/) (2021)
- [CoMoGAN: continuous model-guided image-to-image translation](https://mlsanity.com/paper/2103.06879/) (2021)
- [GAN-based Facial Attribute Manipulation](https://mlsanity.com/paper/2210.12683/) (2022)
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- [Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data](https://mlsanity.com/paper/2203.10474/) (2022)
- [DisUnknown: Distilling Unknown Factors for Disentanglement Learning](https://mlsanity.com/paper/2109.08090/) (2021)
- [FacialGAN: Style Transfer and Attribute Manipulation on Synthetic Faces](https://mlsanity.com/paper/2110.09425/) (2021)
- [Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization](https://mlsanity.com/paper/2112.03163/) (2021)
- [StyleGenes: Discrete and Efficient Latent Distributions for GANs](https://mlsanity.com/paper/2305.00599/) (2023)
- [ChildPredictor: A Child Face Prediction Framework with Disentangled Learning](https://mlsanity.com/paper/2204.09962/) (2022)
- [OWT: A Foundational Organ-Wise Tokenization Framework for Medical Imaging](https://mlsanity.com/paper/2505.04899/) (2025)
- [RoNet: Rotation-oriented Continuous Image Translation](https://mlsanity.com/paper/2404.04474/) (2024)
- [URECA: The Chain of Two Minimum Set Cover Problems exists behind Adaptation to Shifts in Semantic Code Search](https://mlsanity.com/paper/2502.07494/) (2025)

## Abstract (arXiv)

Disentangling factors of variation has become a very challenging problem on representation learning. Existing algorithms suffer from many limitations, such as unpredictable disentangling factors, poor quality of generated images from encodings, lack of identity information, etc. In this paper, we propose a supervised learning model called DNA-GAN which tries to disentangle different factors or attributes of images. The latent representations of images are DNA-like, in which each individual piece (of the encoding) represents an independent factor of the variation. By annihilating the recessive piece and swapping a certain piece of one latent representation with that of the other one, we obtain two different representations which could be decoded into two kinds of images with the existence of the corresponding attribute being changed. In order to obtain realistic images and also disentangled representations, we further introduce the discriminator for adversarial training. Experiments on Multi-PIE and CelebA datasets finally demonstrate that our proposed method is effective for factors disentangling and even overcome certain limitations of the existing methods.