Generative Adversarial Networks

Also known as GAN

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9 papers in the last four weeks, up 13% on the four weeks before. 0.1% of all new papers.

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Latest papers 96

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Cross-scale Aligned Supervision for Training GANs
May 26, 2026Sangeek Hyun, MinKyu Lee, Jae-Pil HeoConditional Image Generation One-Step Generative Modeling
Sungkyunkwan University
Do Synthetic Brain MRIs Reliably Improve Tumour Classification? A StyleGAN2-ADA Class-Plane Augmentation Study on BRISC 2025
May 21, 2026José Rafael Noriega CedeñoSynthetic Data Augmentation Medical Image Classification
Faculty of Applied Sciences and Technology
Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection
May 20, 2026Adam Innan, Mansour El Alami, Nouhaila Innan +2Class-Imbalanced Learning Synthetic Data Augmentation
Quantum Physics and Spintronics Team, LPMC, Faculty of Sciences Ben M’sick, Hassan II University of Casablanca, Morocco · eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE · Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD, Abu Dhabi, UAE
Generative Adversarial Learning from Deterministic Processes
May 18, 2026Joris C. Kühl, Hanno GottschalkDynamical Systems Statistical Learning Theory
Institute of Mathematics, Technical University of Berlin, Germany
When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective
May 15, 2026Yuan-dong Cao, Chi Chiu SO, Jun-Min Wang +1Adversarial Training PDE Surrogate Modeling
School of Mathematics and Statistics, Beijing Institute of Technology, China · Department of Computer Science & UCL AI Centre, University College London, UK · School of Professional Education and Executive Development The Hong Kong Polytechnic University, China
Generative Deep Learning for Computational Destaining and Restaining of Unregistered Digital Pathology Images
May 14, 2026Aarushi Kulkarni, Alarice Lowe, Pratik ShahComputational Pathology Domain Generalization
Department of Computer Science University of California Irvine, CA, USA · Department of Pathology Stanford University Stanford, CA, USA · Department of Pathology and Laboratory Medicine Biomedical Engineering Electrical Engineering and Computer Science University of California Irvine, CA, USA
FSCM: Frequency-Enhanced Spatial-Spectral Coupled Mamba for Infrared Hyperspectral Image Colorization
May 13, 2026Tingting Liu, Yuan Liu, Guiping Chen +2Hyperspectral Imaging Visual SSMs
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China · School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China · School of Instrument and Electronics, North University of China, Taiyuan, 030051, China
Cross Modality Image Translation In Medical Imaging Using Generative Frameworks
May 13, 2026Giulia Romoli, Filippo Ruffini, Francesco Di Feola +20Image-to-Image Translation Medical Image Synthesis
Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Universitetstorget, 4, Umeå, 90187, Sweden · Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, Roma, 00128, Italy · Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, Roma, 00168, Italy +12
Assessment of cloud and associated radiation fields from a GAN stochastic cloud subcolumn generator
May 12, 2026Dongmin Lee, Lazaros Oreopoulos, Nayeong Cho +1Generative Modeling Conditional Generative Modeling
GESTAR-II, Morgan State University, Baltimore, MD, USA. · Earth Sciences Division, NASA’s Goddard Space Flight Center, Greenbelt, MD, USA. · GESTAR-II, University of Maryland – Baltimore County, MD, USA.
Extended Wasserstein-GAN Approach to Causal Distribution Learning: Density-Free Estimation and Minimax Optimality
May 11, 2026Shu Tamano, Masaaki ImaizumiCausal Effect Estimation Causal Counterfactual Generation
1The University of Tokyo · 2Japan Institute for Health Security · 3RIKEN Center for Advanced Intelligence Project +1
Improving Generative Adversarial Networks with Self-Distillation
May 9, 2026Antoni Nowinowski, Krzysztof KrawiecGenerative Adversarial Networks
Poznan University of Technology
NeuroGAN-3D: Enhancing Intrinsic Functional Brain Networks via High-Fidelity 3D Generative Super-Resolution
May 8, 2026M. Moein Esfahani, Sepehr Salem Ghahfarokhi, Mohammed Alser +23D Medical Imaging Medical Image Super-Resolution
1 2 Georgia State University, Atlanta, GA, USA · 2 1 Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
A Paired Point-of-Care Ultrasound Dataset for Image Quality Enhancement and Benchmarking via a cGAN Baseline
May 8, 2026Lennard M. van Karnenbeek, Hilde G. A. van der Pol, Mark Wijkhuizen +5Image-to-Image Translation Ultrasound Imaging
Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands · Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands · Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands +1
Hybrid Quantum-Classical GANs for the Generation of Adversarial Network Flows
May 7, 2026Prateek Paudel, Nitin Jha, Abhishek Parakh +1Adversarial Attacks Network Intrusion Detection
Kennesaw State University, Marietta, GA, USA · University of Nebraska Omaha, Omaha, NE, USA
3D Ultrasound-Derived Pseudo-CT Synthesis Using a Transformer-Augmented Residual Network for Real-Time Operator Guidance
May 6, 2026Sapna Sachan, Amulya Kumar Mahto3D Medical Imaging Ultrasound Imaging
Indian Institute of Technology Guwahati, Assam, PIN 781039 India
Robustness and Transferability of Pix2Geomodel for Bidirectional Facies Property Translation in a Complex Reservoir
May 5, 2026Abdulrahman Al-Fakih, Nabil Sariah, Ardiansyah Koeshidayatullah +2Image-to-Image Translation Generative Adversarial Networks
Department of Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia. · Center for Integrative Petroleum Research, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia
Cross-Domain Adversarial Augmentation: Stabilizing GANs for Medical and Handwriting Data Scarcity
May 3, 2026Md. Sohanuzzaman Soad, Mahady Al Hady, S M Rafiuddin Rifat +1Synthetic Data Augmentation Medical Image Synthesis
Department of Computer Science and Engineering University of Asia Pacific Dhaka, Bangladesh
SRGAN-CKAN: Expressive Super-Resolution with Nonlinear Functional Operators under Minimal Resources
May 2, 2026Roberto Isai Navaro-Aviña, Eduardo Said Merin-Martinez, Andres Mendez-Vazquez +1Kolmogorov-Arnold Networks Generative Adversarial Networks
Cinvestav, Unidad Guadalajara Av. del Bosque 1145, El Bajio Zapopan, Jalisco, 45017, Mexico · Cinvestav, Unidad Tamaulipas Km. 5.5 Carretera Victoria - Soto La Marina Victoria, 87139, Tamaulipas, Mexico
Improving Diversity in Black-box Few-shot Knowledge Distillation
Apr 28, 2026Tri-Nhan Vo, Dang Nguyen, Kien Do +1Synthetic Data Augmentation Few-Shot Learning
Applied Artificial Intelligence Institute (A2I2), Deakin University, Australia
Geometry Preserving Loss Functions Promote Improved Adaptation of Blackbox Generative Model
Apr 26, 2026Sinjini Mitra, Constantine Kyriakakis, Shenyuan Liang +2Domain Adaptation Latent Space Optimization
1Geometric Media Lab, Arizona State University, Tempe 85281, USA. · Department of Statistics, Florida State University, Tallahassee 32306, USA.
A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence
Apr 22, 2026Antoine Heranval, Olivier Lopez, Didier Ngatcha +1Financial Services Time Series Forecasting
BioSP · Biostatistiques et Processus Spatiaux (BioSP), INRAE, Avignon, France · CREST +3
MD-Face: MoE-Enhanced Label-Free Disentangled Representation for Interactive Facial Attribute Editing
Apr 22, 2026Xuan Cui, Yunfei Zhao, Bo Liu +2Disentangled Representation Learning Unsupervised Representation Learning
Pairing Regularization for Mitigating Many-to-One Collapse in GANs
Apr 22, 2026Kuan-Yu Lin, Yu-Chih Huang, Tie LiuGenerative Adversarial Networks Mode Collapse
Institute of Communications Engineering, National Yang Ming Chiao Tung University · Department of Electrical & Computer Engineering, Texas A&M University
Applications of deep generative models to DNA reaction kinetics and to cryogenic electron microscopy
Apr 18, 2026Chenwei ZhangImage Reconstruction Cryo-Electron Microscopy
THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)
IncepDeHazeGAN: Novel Satellite Image Dehazing
Apr 17, 2026Tejeswar Pokuri, Shivarth RaiImage Restoration Image Dehazing
No Institute Given
Enhancing Hazy Wildlife Imagery: AnimalHaze3k and IncepDehazeGan
Apr 17, 2026Shivarth Rai, Tejeswar PokuriSynthetic Data Generation Image Restoration
Department of Computer Science Manipal Institute of Technology, Manipal, India
Synthetic data in cryptocurrencies using generative models
Apr 17, 2026André Saimon S. Sousa, Otto Pires, Frank Acasiete +3Synthetic Data Generation Time Series Generation
Universidade SENAI CIMATEC Salvador, Brazil · CoFi, Universidad Jorge Tadeo Lozano Bogotá, Colombia · Universidade do Estado da Bahia - UNEB Salvador, Brazil
HyPER-GAN: Hybrid Patch-Based Image-to-Image Translation for Real-Time Photorealism Enhancement in Game Engines
Mar 11, 2026Stefanos Pasios, Nikos NikolaidisImage-to-Image Translation Generative Adversarial Networks
School of Informatics, Aristotle University of Thessaloniki (AUTH), Thessaloniki, 54124, Greece
Scaling Quantum Machine Learning without Tricks: Full-Resolution and Diverse Image Generation
Feb 27, 2026Jonas Jäger, Florian J. Kiwit, Carlos A. RiofríoQuantum Generative Modeling Image Generation
BMW Group, Munich, Germany · Department of Computer Science and Institute of Applied Mathematics, University of British Columbia (UBC), Vancouver, Canada · Stewart Blusson Quantum Matter Institute (QMI), Vancouver, Canada +1
PCP-GAN: Property-Constrained Pore-scale image reconstruction via conditional Generative Adversarial Networks
Oct 22, 2025Ali Sadeghkhani, Brandon Bennett, Masoud Babaei +1Conditional Image Generation Image Generation
School of Computer Science, University of Leeds, Leeds, LS2 9JT, UK. · Department of Chemical Engineering, The University of Manchester,2026 Manchester, M13 9PL, UK.

May 26, 2026, cs.CVJ/K move · Enter open · S save

Cross-scale Aligned Supervision for Training GANs

Sangeek Hyun, MinKyu Lee, Jae-Pil Heo

Sungkyunkwan University

Conditional Image Generation One-Step Generative Modeling Generative Adversarial Networks

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Modern GANs often introduce adversarial supervision on intermediate generator outputs and interpret the resulting multi-stage synthesis as coarse-to-fine hierarchical generation. In this work, we challenge this interpretation. We argue that standard scale-wise adversarial supervision does not construct a proper coarse-to-fine hierarchy: each intermediate image is independently pushed toward the real distribution at its own resolution, but this scale-wise realism does not ensure that outputs across stages represent the identical generated sample. Moreover, the scale-specific image produced at each stage is not used as an explicit refinement target for the subsequent stage. Therefore, its adversarial loss can improve a scale-specific output without constraining later stages to preserve the same sample trajectory, allowing them to move toward a different sample rather than refine the previous output. We refer to this problem as a cross-scale trajectory misalignment problem. To resolve it, we propose CAT, a Cross-scale Aligned Transformer for multi-scale adversarial generation. CAT keeps the discriminator scale-wise, so each intermediate output is evaluated at its own resolution, while adding a simple generator-side consistency regularization that aligns intermediate outputs with the final output. On class-conditional ImageNet-256, CAT-H/2 achieves an FID-50K of 1.56 with one-step inference after only 60 training epochs, outperforming strong one-step GAN and diffusion/flow baselines.

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