# OneGAN: Simultaneous Unsupervised Learning of Conditional Image Generation, Foreground Segmentation, and Fine-Grained Clustering

arXiv: https://arxiv.org/abs/1912.13471 · HTML: https://mlsanity.com/paper/1912.13471/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2019.

## Built on by (most cited first, 28 of 28)

- [Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization](https://mlsanity.com/paper/2205.07839/) (2022)
- [Diffusion Models for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2306.09316/) (2023)
- [Improving Object-centric Learning with Query Optimization](https://mlsanity.com/paper/2210.08990/) (2022)
- [Labels4Free: Unsupervised Segmentation using StyleGAN](https://mlsanity.com/paper/2103.14968/) (2021)
- [Finding an Unsupervised Image Segmenter in Each of Your Deep Generative Models](https://mlsanity.com/paper/2105.08127/) (2021)
- [Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion](https://mlsanity.com/paper/2205.07844/) (2022)
- [Unsupervised Foreground Extraction via Deep Region Competition](https://mlsanity.com/paper/2110.15497/) (2021)
- [DeepCut: Unsupervised Segmentation using Graph Neural Networks Clustering](https://mlsanity.com/paper/2212.05853/) (2022)
- [MOVE: Unsupervised Movable Object Segmentation and Detection](https://mlsanity.com/paper/2210.07920/) (2022)
- [DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images](https://mlsanity.com/paper/2104.01325/) (2021)
- [TKG-DM: Training-free Chroma Key Content Generation Diffusion Model](https://mlsanity.com/paper/2411.15580/) (2024)
- [Contrastive Fine-grained Class Clustering via Generative Adversarial Networks](https://mlsanity.com/paper/2112.14971/) (2021)
- [Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior](https://mlsanity.com/paper/2305.19550/) (2023)
- [LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-training](https://mlsanity.com/paper/2308.11239/) (2023)
- [Learning Foreground-Background Segmentation from Improved Layered GANs](https://mlsanity.com/paper/2104.00483/) (2021)
- [PaintSeg: Training-free Segmentation via Painting](https://mlsanity.com/paper/2305.19406/) (2023)
- [Refine and Represent: Region-to-Object Representation Learning](https://mlsanity.com/paper/2208.11821/) (2022)
- [Extracting Semantic Knowledge from GANs with Unsupervised Learning](https://mlsanity.com/paper/2211.16710/) (2022)
- [Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models](https://mlsanity.com/paper/2212.14306/) (2022)
- [SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA](https://mlsanity.com/paper/2303.16666/) (2023)
- [Revisiting Surgical Instrument Segmentation Without Human Intervention: A Graph Partitioning View](https://mlsanity.com/paper/2408.14789/) (2024)
- [CoDeGAN: Contrastive Disentanglement for Generative Adversarial Network](https://mlsanity.com/paper/2103.03636/) (2021)
- [ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation](https://mlsanity.com/paper/2211.13974/) (2022)
- [Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images](https://mlsanity.com/paper/2303.05639/) (2023)
- [SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation](https://mlsanity.com/paper/2301.00366/) (2023)
- [GoogLe2Net: Going Transverse with Convolutions](https://mlsanity.com/paper/2301.00424/) (2023)
- [Seg-HGNN: Unsupervised and Light-Weight Image Segmentation with Hyperbolic Graph Neural Networks](https://mlsanity.com/paper/2409.06589/) (2024)
- [Universal Guideline-Driven Image Clustering via a Hybrid LLM Agent](https://mlsanity.com/paper/2606.24094/) (2026)

## Abstract (arXiv)

We present a method for simultaneously learning, in an unsupervised manner, (i) a conditional image generator, (ii) foreground extraction and segmentation, (iii) clustering into a two-level class hierarchy, and (iv) object removal and background completion, all done without any use of annotation. The method combines a Generative Adversarial Network and a Variational Auto-Encoder, with multiple encoders, generators and discriminators, and benefits from solving all tasks at once. The input to the training scheme is a varied collection of unlabeled images from the same domain, as well as a set of background images without a foreground object. In addition, the image generator can mix the background from one image, with a foreground that is conditioned either on that of a second image or on the index of a desired cluster. The method obtains state of the art results in comparison to the literature methods, when compared to the current state of the art in each of the tasks.