Data to 7 Oct 2026

Semi-Supervised Learning with Generative Adversarial Networks

arXiv 1606.01583 ↗First posted 5 Jun 2016stat.ML cs.LGcited by 103

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Abstract (arXiv)

We extend Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels. We train a generative model G and a discriminator D on a dataset with inputs belonging to one of N classes. At training time, D is made to predict which of N+1 classes the input belongs to, where an extra class is added to correspond to the outputs of G. We show that this method can be used to create a more data-efficient classifier and that it allows for generating higher quality samples than a regular GAN.

Key facts. Cited by 103 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jun 2016.

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Citing papers per year: 2021: 34; 2022: 29; 2023: 18; 2024: 12; 2025: 7; 2026: 3.

103 papers.

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2022Generative Adversarial Networkscited by 5,603
2021Score-based Generative Modeling in Latent Spacecited by 634
2021A Survey on Deep Semi-supervised Learningcited by 250
2021Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizationcited by 141
2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
2022USB: A Unified Semi-supervised Learning Benchmark for Classificationcited by 95
2022Synthetic Data -- what, why and how?cited by 66
2022Debiased Self-Training for Semi-Supervised Learningcited by 62
2021Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Imagescited by 58
2021Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advancescited by 54
2021Semi-Supervised Action Recognition with Temporal Contrastive Learningcited by 50
2021CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learningcited by 41

38 more

2022Recent Advances for Quantum Neural Networks in Generative Learningcited by 39
2021DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Networkcited by 37
2024Generative Artificial Intelligence: A Systematic Review and Applicationscited by 37
2022Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Datacited by 36
2021Revisiting Consistency Regularization for Semi-Supervised Learningcited by 34
2021Generating 3D structures from a 2D slice with GAN-based dimensionality expansioncited by 29
2022Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysiscited by 29
2021Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesiscited by 24
2021Creativity and Machine Learning: A Surveycited by 21
2021AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learningcited by 21
2021Conditional GANs with Auxiliary Discriminative Classifiercited by 20
2021On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competitioncited by 18
2021GAN-based Reactive Motion Synthesis with Class-aware Discriminators for Human-human Interactioncited by 18
2021LARGE: Latent-Based Regression through GAN Semanticscited by 17
2022Semantic-shape Adaptive Feature Modulation for Semantic Image Synthesiscited by 16
2022Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Studentscited by 16
2022Adversarial Unlearning: Reducing Confidence Along Adversarial Directionscited by 14
2022ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseasescited by 14
2023Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directionscited by 14
2023SemiReward: A General Reward Model for Semi-supervised Learningcited by 14
2022A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sourcescited by 13
2022Two Decades of Bengali Handwritten Digit Recognition: A Surveycited by 13
2021A Comparative Review of Recent Few-Shot Object Detection Algorithmscited by 10
2023Virtual Category Learning: A Semi-Supervised Learning Method for Dense Prediction with Extremely Limited Labelscited by 10
2022RareGAN: Generating Samples for Rare Classescited by 9
2021Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentationcited by 8
2021ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networkscited by 8
2023Instructor-inspired Machine Learning for Robust Molecular Property Predictioncited by 8
2021Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluationcited by 7
2021Improve Learning from Crowds via Generative Augmentationcited by 6
2022SemiCurv: Semi-Supervised Curvilinear Structure Segmentationcited by 6
2022Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classificationcited by 5
2021CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Trainingcited by 4
2021Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Surveycited by 4
2022Semi-supervised Bladder Tissue Classification in Multi-Domain Endoscopic Imagescited by 4
2023Wafer Map Defect Patterns Semi-Supervised Classification Using Latent Vector Representationcited by 4
2024Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillationcited by 4
2022Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Faccade Modelingcited by 3

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