Improving 3D deep learning segmentation with biophysically motivated cell synthesis by Roman Bruch, Mario Vitacolonna, Elina Nürnberg, Simeon Sauer, Rüdiger Rudolf, Markus Reischl

“…Furthermore, we present a generative adversarial network (GAN) training scheme that generates not only image data but also matching labels. …”
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A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant by Shwetha V., Arnav Bhagwat, Vijaya Laxmi, Sakshi Shrivastava

“…In this study, we harness the power of a deep convolutional generative adversarial network (DCGAN) to generate a dataset of jasmine plant leaf disease images. …”
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Unsupervised Image Super-Resolution for High-Resolution Satellite Imagery via Omnidirectional Real-to-Synthetic Domain Translation by Minkyung Chung, Yongil Kim

“…Our model, the bicubic-downsampled LR image-guided generative adversarial network for unsupervised learning (BLG-GAN-U), divides the SR process into two stages: LR image domain translation and image super-resolution. …”
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WGAN-GP for Synthetic Retinal Image Generation: Enhancing Sensor-Based Medical Imaging for Classification Models by Héctor Anaya-Sánchez, Leopoldo Altamirano-Robles, Raquel Díaz-Hernández, Saúl Zapotecas-Martínez

“…In this work, we propose a novel method using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and nearest-neighbor interpolation to generate high-quality synthetic images for diabetic retinopathy classification. …”
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Robustly detecting mpox and non-mpox using a deep learning framework based on image inpainting by Yujun Cao, Yubiao Yue, Xiaoming Ma, Di Liu, Rongkai Ni, Haihua Liang, Zhenzhang Li

“…In MIM’s pipeline, a generative adversarial network learns feature representations of mpox images by inpainting the masked mpox images. …”
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Fault Diagnosis of Magnetically Controlled On-Column Circuit Breaker Based on Small Sample Condition by He Tian, Chao Liang, Wenpeng Ma, Tianchang Zhang

“…Initially, a Variational Autoencoder (VAE) is employed to extract the latent distribution of genuine samples, which are then integrated with the Auxiliary Classifier Generative Adversarial Network (ACGAN) generator to learn the characteristics of real data. …”
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Mask-Pix2Pix Network for Overexposure Region Recovery of Solar Image by Dong Zhao, Long Xu, Linjie Chen, Yihua Yan, Ling-Yu Duan

“…It is built on a well-known Pix2Pix network of conditional generative adversarial network (cGAN). In addition, a hybrid loss function, including an adversarial loss, a masked L1 loss and a edge mass loss/smoothness, are integrated together for addressing challenges of overexposure relative to conventional image restoration. …”
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Personalized Federated Learning for Heterogeneous Residential Load Forecasting by Xiaodong Qu, Chengcheng Guan, Gang Xie, Zhiyi Tian, Keshav Sood, Chaoli Sun, Lei Cui

“…Based on the principle of generative adversarial network (GAN), the algorithm achieves the balance between privacy and prediction accuracy throughout the game. …”
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On improving interoperability for cross‐domain multi‐finger fingerprint matching using coupled adversarial learning by Md Mahedi Hasan, Nasser Nasrabadi, Jeremy Dawson

“…In fact, using a conditional coupled generative adversarial network, the authors project both the contactless and the contact‐based fingerprint into a latent subspace to explore the hidden relationship between them using class‐specific contrastive loss and ArcFace loss. …”
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Discriminative, generative artificial intelligence, and foundation models in retina imaging by Paisan Ruamviboonsuk, Niracha Arjkongharn, Nattaporn Vongsa, Pawin Pakaymaskul, Natsuda Kaothanthong

“…Many studies found better performance of ViT, compared to CNN, for common tasks such as diabetic retinopathy screening on color fundus photographs (CFP) and segmentation of retinal fluid on optical coherence tomography (OCT) images. Generative Adversarial Network (GAN) is the main AI technique in generative AI in retinal imaging. …”
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Inversion-Based Face Swapping With Diffusion Model by Daehyun Yoo, Hongchul Lee, Jiho Kim

“…Previous studies have primarily utilized generative adversarial network-based models for face swapping. …”
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Deep Learning‐Based Regional Ionospheric Total Electron Content Prediction—Long Short‐Term Memory (LSTM) and Convolutional LSTM Approach by Se‐Heon Jeong, Woo Kyoung Lee, Hyosub Kil, Soojeong Jang, Jeong‐Heon Kim, Young‐Sil Kwak

“…Both the LSTM and ConvLSTM models are trained using the complete regional TEC maps reconstructed by applying the Deep Convolutional Generative Adversarial Network–Poisson Blending (DCGAN‐PB) method to observed TEC data. …”
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Global TEC Map Fusion Through a Hybrid Deep Learning Model: RFGAN by Zhou Chen, Kecheng Zhou, Haimeng Li, Jing‐song Wang, Zhihai Ouyang, Xiaohua Deng

“…Different from the previous methods, in the study, a deep learning hybrid model (RFGAN) based on Dual‐Discriminator Conditional Generative Adversarial Network (DDcGAN) and Free‐Form Image Inpainting with Gated Convolution (Deepfill v2) is proposed to fuse the Massachusetts Institute of Technology (MIT)—TEC, International Global Navigation Satellite System TEC (IGS‐TEC) and altimetry satellite TEC. …”
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TEC Map Completion Using DCGAN and Poisson Blending by Yang Pan, Mingwu Jin, Shunrong Zhang, Yue Deng

“…Among them, deep convolutional generative adversarial network (DCGAN) is capable of learning the properties of the objects and recovering missing data effectively. …”
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Computer-aided cholelithiasis diagnosis using explainable convolutional neural network by Dheeraj Kumar, Mayuri A. Mehta, Ketan Kotecha, Ambarish Kulkarni

“…Second, a modified deep convolutional generative adversarial network is proposed to produce synthetic ultrasound images for better model generalization. …”
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The construction of a digital dissemination platform for the intangible cultural heritage using convolutional neural network models by Zhurong Liu

“…The platform integrates a range of advanced technologies, including the Densely Connected Convolutional Networks - Bottleneck and Compression model, a notable convolutional neural network, along with natural language processing algorithms, generative adversarial network algorithms, and neural collaborative filtering algorithms. …”
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Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection by Qingming Ye, Zhilu Wang, Yi Lou, Yang Yang, Jue Hou, Zheng Liu, Weiguang Liu, Jiayu Li

“…The MPR framework combines a CNN for automatic feature extraction with a multiscale generative adversarial network (GAN) to model skeletal integrity using healthy samples. …”
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Species-Level Saltmarsh Vegetation Fractional Cover Estimation Based on Time Series Sentinel-2 Imagery with the Assistance of Sample Expansion by Jinghan Sha, Zhaojun Zhuo, Qingqing Zhou, Yinghai Ke, Mengyao Zhang, Jinyuan Li, Yukui Min

“…In this study, we proposed a Fractional Vegetation Cover Wasserstein Generative Adversarial Network (FVC-WGAN) model for FVC sample expansion. …”
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Metagenomic exploration and computational prediction of novel enzymes for polyethylene terephthalate degradation by Donya Afshar Jahanshahi, Mohammad Reza Rezaei Barzani, Mohammad Bahram, Shohreh Ariaeenejad, Kaveh Kavousi

“…To overcome the scarcity of known PET-degrading enzymes, we used a Generative Adversarial Network (GAN) model for dataset augmentation and a pretrained deep Evolutionary Scale Language Model (ESM) to generate sequence embeddings for classification. …”
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Research on Density Peak Clustering Color-Separation Algorithm Based on SOM Neural Network by Xin Ru, Yanhao Wang, Ran Chen, Zhifa Zeng, Laihu Peng, Wei Tang, Weimin Shi, Hui Wang

“…To solve these problems, In this paper, we propose a density peak clustering color-separation algorithm based on self-organizing mapping (SOM) neural network, which firstly uses enhanced super-resolution generative adversarial network (Real-ESRGAN) blind super-resolution reconstruction network to clarify the degraded image and obtain a high-resolution image with clear boundaries; secondly, we carry out the initial clustering through the SOM neural network to simplify the image information; and then we use an improved density peak clustering (DPC) algorithm to calculate clustering centers under the conditions of conforming to the perception of human eyes, and carry out secondary clustering on the image; finally, carry out image post-processing through the variegated spots merging algorithm based on the connected component analysis. …”
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