# OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization

arXiv: https://arxiv.org/abs/1905.10836 · HTML: https://mlsanity.com/paper/1905.10836/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

## Built on by (most cited first, 17 of 17)

- [Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis](https://mlsanity.com/paper/2101.04775/) (2021)
- [WarpedGANSpace: Finding non-linear RBF paths in GAN latent space](https://mlsanity.com/paper/2109.13357/) (2021)
- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [EigenGAN: Layer-Wise Eigen-Learning for GANs](https://mlsanity.com/paper/2104.12476/) (2021)
- [Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training](https://mlsanity.com/paper/2103.00673/) (2021)
- [medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space](https://mlsanity.com/paper/2204.05376/) (2022)
- [Cluster-guided Image Synthesis with Unconditional Models](https://mlsanity.com/paper/2112.12911/) (2021)
- [Deep Curvilinear Editing: Commutative and Nonlinear Image Manipulation for Pretrained Deep Generative Model](https://mlsanity.com/paper/2211.14573/) (2022)
- [CoDeGAN: Contrastive Disentanglement for Generative Adversarial Network](https://mlsanity.com/paper/2103.03636/) (2021)
- [Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models](https://mlsanity.com/paper/2204.08573/) (2022)
- [DOT-VAE: Disentangling One Factor at a Time](https://mlsanity.com/paper/2210.10920/) (2022)
- [Inference-InfoGAN: Inference Independence via Embedding Orthogonal Basis Expansion](https://mlsanity.com/paper/2110.00788/) (2021)
- [Delta-GAN-Encoder: Encoding Semantic Changes for Explicit Image Editing, using Few Synthetic Samples](https://mlsanity.com/paper/2111.08419/) (2021)
- [ProtoVAE: Prototypical Networks for Unsupervised Disentanglement](https://mlsanity.com/paper/2305.09092/) (2023)
- [Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization](https://mlsanity.com/paper/2410.20573/) (2024)
- [Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model](https://mlsanity.com/paper/2502.01191/) (2025)
- [Sparsification and Reconstruction from the Perspective of Representation Geometry](https://mlsanity.com/paper/2505.22506/) (2025)

## Abstract (arXiv)

Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel GAN-based disentanglement framework with One-Hot Sampling and Orthogonal Regularization (OOGAN). While previous works mostly attempt to tackle disentanglement learning through VAE and seek to implicitly minimize the Total Correlation (TC) objective with various sorts of approximation methods, we show that GANs have a natural advantage in disentangling with an alternating latent variable (noise) sampling method that is straightforward and robust. Furthermore, we provide a brand-new perspective on designing the structure of the generator and discriminator, demonstrating that a minor structural change and an orthogonal regularization on model weights entails an improved disentanglement. Instead of experimenting on simple toy datasets, we conduct experiments on higher-resolution images and show that OOGAN greatly pushes the boundary of unsupervised disentanglement.