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Deeper, Sharper, Faster: Application of Efficient Transformer to Galaxy Image Restoration

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Ease. ML: A Lifecycle Management System for Machine Learning

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Stellar Cluster Detection Using GMM with Deep Variational Autoencoder

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Performing Group Difference Testing on Graph Structured Data From GANs: Analysis and Applications in Neuroimaging

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RGAN: Rényi Generative Adversarial Network

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Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation

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Cross-survey Image Transformation: Enhancing SDSS and DECaLS Images to Near-HSC Quality for Advanced Astronomical Analysis

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Comparing GANs for Translating Satellite Images to Maps

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Study Of AI Generated And Real Face Perception

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Image restoration with point-spread function regularization and active learning

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An Overreaction to the Broken Machine Learning Abstraction

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Conditional Generative Adversarial Network Enabled Localized Stress Recovery of Periodic Composites

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Probing Neural Networks for the Gamma/Hadron Separation of the Cherenkov Telescope Array

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Using a Conditional Generative Adversarial Network to Control the Statistical Characteristics of Generated Images for IACT Data Analysis

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Training Generative Adversarial Networks With Weights

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Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks

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Cosmic voids in GAN-generated maps of large-scale structure

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Hybrid Classical-Quantum Generative Algorithms for Financial Modelling and Prediction

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A versatile framework for analyzing galaxy image data by incorporating Human-in-the-loop in a large vision model*

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An Image Quality Evaluation and Masking Algorithm Based On Pretrained Deep Neural Networks

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Joint Deconvolution of Astronomical Images in the Presence of Poisson Noise

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Galaxy imaging with generative models: insights from a two-models framework

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Surface Defect Detection Based on ResNet Classification Network with GAN Optimized

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Multimodal Deep Learning for Two-Year ENSO Forecast

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Automated Interpretability Scoring of Ground-Based Observations of LEO Objects with Deep Learning

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Ease.ML: A Lifecycle Management System for MLDev and MLOps

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Astronomical image denoising based on Convolutional Neural Network

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Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meterPreprint

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Astronomical Image Quality Assessment Based on Deep Learning for Resource-constrained Environments

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Searching for Subsecond Stellar Variability with Wide-field Star Trails and Deep Learning

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How the spectral energy distribution and galaxy morphology constrain each other, with application to morphological selection using galaxy colours

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Classifying CMB time-ordered data through deep neural networks

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A possible converter to denoise the images of exoplanet candidates through machine learning techniques

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CoaddNet: Enhancing signal-to-noise ratio in single-shot images using convolutional neural networks with coadded image effect

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Demystifying SAR with attention

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Efficient transformer-augmented U-net variants for correcting seeing effects in astronomy

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Investigation on deep learning-based galaxy image translation models

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Multiqubit state learning with entangling quantum generative adversarial networks

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Deblurring galaxy images with Tikhonov regularization on magnitude domain

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Restoration of Spatially Variant Blurred Images with Wide-Field Telescope Based on Deep Learning

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Large-field Astronomical Image Restoration and Superresolution Reconstruction using Deep Learning

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DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data

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Fast Generation of Large-scale Structure Density Maps via Generative Adversarial Networks

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A study of neural networks point source extraction on simulated Fermi/ LAT telescope images

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Time-delay estimation in unresolved lensed quasars

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Deep learning enables rapid and robust analysis of fluorescence lifetime imaging in photon-starved conditionsPreprint

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Statistical inference for generative adversarial networksPreprint

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Instrument-To-Instrument translation: Instrumental advances drive restoration of solar observation series via deep learningPreprint

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Can Artificial Intelligence be Used to Improve Productivity by Automating Elements of the User Experience Design Processes?Preprint

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A physics-informed, dual-domain framework for astronomical image deconvolution

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Computational imaging towards next-generation optical observational astronomy

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AI for science: Progress, challenges, and perspectives

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Stacked Bidirectional Convolutional LSTMs for 3D Non-contrast CT Reconstruction from Spatiotemporal 4D CT

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Enhanced Image Reconstruction with Quality-Weighted Iterative Deconvolution (QWID)

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Image Classification Improvement: Text-to-Image AI for Synthetic Dataset Approach

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Reconstructing the mid-infrared spectra of galaxies using ultraviolet to submillimeter photometry and Deep Generative Networks

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Research on human-machine collaborative innovation methods in design education inspired by the learning mechanism of generative adversarial networks

Zeng, S., Lan, S., Xu, Z., Hu, S., Li, M., Liu, S., & Chen, T. (2025) · Design and Artificial Intelligence · doi:10.1016/j.daai.2025.100020 · Cited by 4

A Machine-learning Approach to Assessing the Presence of Substructure in Quasar-host Galaxies Using the Hyper Suprime-cam Subaru Strategic Program

Nagele, C., Silverman, J. D., Hartwig, T., Li, J., Bottrell, C., Ding, X., & Toba, Y. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acbd4a · Cited by 2

QUOTAS: A New Research Platform for the Data-driven Discovery of Black Holes

Natarajan, P., Tang, K. S., McGibbon, R. T., Khochfar, S., Nord, B., Sigurðsson, S., Tricot, J., Cappelluti, N., George, D., & Hidary, J. D. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acd9ce · Cited by 6

Generating Synthetic Images of Gamma-Ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks

Dubenskaya, Y., Kryukov, A., Demichev, A., Polyakov, S., Zhurov, D., Gres, E., & Vlaskina, A. (2023) · Moscow University Physics Bulletin · doi:10.3103/s0027134923070056 · Cited by 1

Perspectives and limitations of visible-thermal image pair synthesis via generative adversarial networks

Panchard, D., Marelli, F., Presa, E. D. M., Wellig, P., & Liebling, M. (2021) · Target and Background Signatures · doi:10.1117/12.2599889 · Cited by 2

Quantum computing enhanced machine learning for physico-chemical applicationsPreprint

Sajjan, M., Li, J., Selvarajan, R., Sureshbabu, S. H., Kale, S. S., Gupta, R., & Kais, S. (2021) · arXiv (Cornell University) · Cited by 3

Detecting and analysing the topology of the cosmic web with spatial clustering algorithms I: methods

Kelesis, D., Basilakos, S., Lesta, V. P., Fotakis, D. A., & Efstathiou, A. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac2444 · Cited by 1

Radio halo detection in MWA data using deep neural networks and generative data augmentation

Mishra, A. K., Tolley, E., Krishna, S. P., & Kneib, J. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf467 · Cited by 4

Machine learning for workpiece mass prediction using real and synthetic acoustic data

Whittaker, D. S., Gregório, J., & Byrne, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-03018-3 · Cited by 1

NPSCA-CGAN: a signal processing framework for enhanced non-profiled side-channel attacks

Tang, C., Li, L., & Ou, Y. (2025) · Cluster Computing · doi:10.1007/s10586-025-05401-1 · Cited by 1

Cosmic-ray Detection and Rejection for CSST

Yu, Y., Ma, B., Zhang, T., Hu, Y., & Zhang, Y. (2025) · The Astronomical Journal · doi:10.3847/1538-3881/ae1b96 · Cited by 1

GalaxyGAN: Generative Adversarial Networks for recovery of galaxy features

Schawinski, K., Zhang, C., Zhang, H., Fowler, L., & Santhanam, G. K. (2017) · Astrophysics Source Code Library

Recovering galaxy images from noisy data

Smith, K. T. (2017) · Science · doi:10.1126/science.355.6331.1278-e

Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networks

Fussell, L., & Moews, B. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz602 · Cited by 43

Solar image deconvolution by generative adversarial network

Xu, L., Sun, W.-Q., Yan, Y.-H., & Zhang, W.-Q. (2020) · Research in Astronomy and Astrophysics · doi:10.1088/1674-4527/20/11/170 · Cited by 17

Deep learning-based galaxy image deconvolution

Akhaury, U., Starck, J., Jablonka, P., Courbin, F., & Michalewicz, K. (2022) · Frontiers in Astronomy and Space Sciences · doi:10.3389/fspas.2022.1001043 · Cited by 21