mlsanityData to 7 Oct 2026

Semantic Segmentation using Adversarial Networks

arXiv 1611.08408 ↗First posted 25 Nov 2016cs.CVcited by 73

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Abstract (arXiv)

Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic segmentation models. We train a convolutional semantic segmentation network along with an adversarial network that discriminates segmentation maps coming either from the ground truth or from the segmentation network. The motivation for our approach is that it can detect and correct higher-order inconsistencies between ground truth segmentation maps and the ones produced by the segmentation net. Our experiments show that our adversarial training approach leads to improved accuracy on the Stanford Background and PASCAL VOC 2012 datasets.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Nov 2016.

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Citing papers per year: 2020: 1; 2021: 33; 2022: 19; 2023: 8; 2024: 6; 2025: 4; 2026: 2.

73 papers.

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  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2021SegDiff: Image Segmentation with Diffusion Probabilistic Modelscited by 279
  3. 2021DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effortcited by 264
  4. 2021Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizationcited by 141
  5. 2022Highly Accurate Dichotomous Image Segmentationcited by 110
  6. 2022Class-Aware Adversarial Transformers for Medical Image Segmentationcited by 33
  7. 2021Medical Image Segmentation with Limited Supervision: A Review of Deep Network Modelscited by 21
  8. 2021Multi-Exit Semantic Segmentation Networkscited by 18
  9. 2023A Survey on Semi-Supervised Semantic Segmentationcited by 16
  10. 2021AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsingcited by 15
  11. 2021Generative Adversarial Networks: A Survey Towards Private and Secure Applicationscited by 15
  12. 2021Data synthesis and adversarial networks: A review and meta-analysis in cancer imagingcited by 15
38 more
  1. 2022Marior: Margin Removal and Iterative Content Rectification for Document Dewarping in the Wildcited by 15
  2. 2021Generative Modeling for Multi-task Visual Learningcited by 14
  3. 2022Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Expertscited by 13
  4. 2024Exploring Phrase-Level Grounding with Text-to-Image Diffusion Modelcited by 13
  5. 2021Secure Domain Adaptation with Multiple Sourcescited by 12
  6. 2021Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddingscited by 10
  7. 2024Flattening the Parent Bias: Hierarchical Semantic Segmentation in the Poincar'e Ballcited by 9
  8. 2021Energy-Based Generative Cooperative Saliency Predictioncited by 8
  9. 2022Multi-Class Segmentation from Aerial Views using Recursive Noise Diffusioncited by 8
  10. 2025PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomaliescited by 8
  11. 2021Tracking by Joint Local and Global Search: A Target-aware Attention based Approachcited by 7
  12. 2021Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectivescited by 7
  13. 2021Super Neuronscited by 7
  14. 2022PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Imagescited by 7
  15. 2021Learning to Address Intra-segment Misclassification in Retinal Imagingcited by 6
  16. 2021PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnosticscited by 5
  17. 2022A study of deep perceptual metrics for image quality assessmentcited by 5
  18. 2022Transformer based Generative Adversarial Network for Liver Segmentationcited by 5
  19. 2023Transferable Attack for Semantic Segmentationcited by 4
  20. 2023Efficient Remote Sensing Segmentation With Generative Adversarial Transformercited by 4
  21. 2021Semantic Segmentation by Improved Generative Adversarial Networkscited by 3
  22. 2021Edge-competing Pathological Liver Vessel Segmentation with Limited Labelscited by 3
  23. 2024Frequency-based Matcher for Long-tailed Semantic Segmentationcited by 3
  24. 2025Self-Supervised Learning for Image Segmentation: A Comprehensive Surveycited by 3
  25. 2021Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentationcited by 2
  26. 2021Semi-Supervised Segmentation of Concrete Aggregate Using Consensus Regularisation and Prior Guidancecited by 2
  27. 2021Generative Adversarial Network: Some Analytical Perspectivescited by 2
  28. 2021Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learningcited by 2
  29. 2021Convergence of GANs Training: A Game and Stochastic Control Methodologycited by 2
  30. 2023Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imagingcited by 2
  31. 2021Comparing Deep Learning strategies for paired but unregistered multimodal segmentation of the liver in T1 and T2-weighted MRIcited by 1
  32. 2021Visual Boundary Knowledge Translation for Foreground Segmentationcited by 1
  33. 2021Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulationscited by 1
  34. 2022Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection systemcited by 1
  35. 2022ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate Particlescited by 1
  36. 2022Exploration of Convolutional Neural Network Architectures for Large Region Map Automationcited by 1
  37. 2023A Dual Attentive Generative Adversarial Network for Remote Sensing Image Change Detectioncited by 1
  38. 2023Task-driven Prompt Evolution for Foundation Modelscited by 1

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