Enhancing neuromolecular imaging classification in low-data regimes with generative machine learning: A case study in HDAC PET/MR imaging of alcohol use disorder by Tyler N. Meyer, Olga Andreeva, Roger D. Weiss, Wei Ding, Iris Shen, Changning Wang, Ping Chen, Tewodros Mulugeta Dagnew

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MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans by Yu Lyu, Xiaolin Tian

“…To address this challenge, we propose multiple tasking Wasserstein Generative Adversarial Network U-shape Network++ (MWG-UNet++) to brain tumor segmentation by integrating a U-Net architecture enhanced with transformer layers which combined with Wasserstein Generative Adversarial Networks (WGAN) for data augmentation. …”
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ERNIE-TextCNN: research on classification methods of Chinese news headlines in different situations by Yumin Yan

“…Firstly, the dataset is expanded using AEDA data augmentation technology based on word-level information. …”
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Research on Super-Resolution Reconstruction of Coarse Aggregate Particle Images for Earth–Rock Dam Construction Based on Real-ESRGAN by Shuangping Li, Lin Gao, Bin Zhang, Zuqiang Liu, Xin Zhang, Linjie Guan, Junxing Zheng

“…To improve the generalization ability of the super-resolution model, the study expands the morphological database of earth/rock dam particles by employing a multi-modal data augmentation strategy, covering a variety of particle shapes. …”
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Driver Drowsiness Detection Using Swin Transformer and Diffusion Models for Robust Image Denoising by Samy Abd El-Nabi, Ahmed F. Ibrahim, El-Sayed M. El-Rabaie, Osama F. Hassan, Naglaa F. Soliman, Khalil F. Ramadan, Walid El-Shafai

“…Moreover, a detailed sensitivity analysis of data augmentation strategies reveals that techniques such as rotation and horizontal flip substantially enhance the model’s generalization across variable visual inputs. …”
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Exploring CycleGAN Technique for Improved Plant Disease Detection and Analysis by Ouyang Luyi

“…This paper tackles the problem by using Cycle-Consistent General Adversarial Networks (CycleGAN) to create artificial images of diseased plant leaves. …”
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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…Additionally, features were enhanced by applying noise reduction techniques via magnitude spectral subtraction and feature optimization, reflecting the characteristics of rotating equipment. Furthermore, data were augmented using generative adversarial networks to prevent overfitting. …”
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