mlsanityData to 14 Oct 2026

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

arXiv 1912.13471 ↗First posted 31 Dec 2019cs.CVcited by 28

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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.

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

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Citing papers per year: 2021: 7; 2022: 9; 2023: 8; 2024: 3; 2025: 0; 2026: 1.

28 papers.

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  1. 2022Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localizationcited by 150
  2. 2023Diffusion Models for Open-Vocabulary Segmentationcited by 64
  3. 2022Improving Object-centric Learning with Query Optimizationcited by 60
  4. 2021Labels4Free: Unsupervised Segmentation using StyleGANcited by 53
  5. 2021Finding an Unsupervised Image Segmenter in Each of Your Deep Generative Modelscited by 44
  6. 2022Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motioncited by 42
  7. 2021Unsupervised Foreground Extraction via Deep Region Competitioncited by 26
  8. 2022DeepCut: Unsupervised Segmentation using Graph Neural Networks Clusteringcited by 25
  9. 2022MOVE: Unsupervised Movable Object Segmentation and Detectioncited by 20
  10. 2021DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Imagescited by 14
  11. 2024TKG-DM: Training-free Chroma Key Content Generation Diffusion Modelcited by 11
  12. 2021Contrastive Fine-grained Class Clustering via Generative Adversarial Networkscited by 9
16 more
  1. 2023Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Priorcited by 8
  2. 2023LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-trainingcited by 8
  3. 2021Learning Foreground-Background Segmentation from Improved Layered GANscited by 7
  4. 2023PaintSeg: Training-free Segmentation via Paintingcited by 7
  5. 2022Refine and Represent: Region-to-Object Representation Learningcited by 5
  6. 2022Extracting Semantic Knowledge from GANs with Unsupervised Learningcited by 5
  7. 2022Foreground-Background Separation through Concept Distillation from Generative Image Foundation Modelscited by 5
  8. 2023SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTAcited by 4
  9. 2024Revisiting Surgical Instrument Segmentation Without Human Intervention: A Graph Partitioning Viewcited by 3
  10. 2021CoDeGAN: Contrastive Disentanglement for Generative Adversarial Networkcited by 2
  11. 2022ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentationcited by 2
  12. 2023Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Imagescited by 2
  13. 2023SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentationcited by 1
  14. 2023GoogLe2Net: Going Transverse with Convolutionscited by 0
  15. 2024Seg-HGNN: Unsupervised and Light-Weight Image Segmentation with Hyperbolic Graph Neural Networkscited by 0
  16. 2026Universal Guideline-Driven Image Clustering via a Hybrid LLM Agentcited by 0