Results 1 to 10 of about 90,097 (117)
Deep reinforcement learning enables adaptive-image augmentation for automated optical inspection of plant rust [PDF]
Frontiers in Plant Science, 2023 This study proposes an adaptive image augmentation scheme using deep reinforcement learning (DRL) to improve the performance of a deep learning-based automated optical inspection system.
Shiyong Wang +7 more
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Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation
Applied Sciences, 2023 The deep learning-based image segmentation approach has evolved into the mainstream of target detection and shape characterization in microscopic image analysis. However, the accuracy and generalizability of deep learning approaches are still hindered by
Jingchao Ma +5 more
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SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map
Sensors, 2021 Modern data augmentation strategies such as Cutout, Mixup, and CutMix, have achieved good performance in image recognition tasks. Particularly, the data augmentation approaches, such as Mixup and CutMix, that mix two images to generate a mixed training ...
Jaehyeop Choi +3 more
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Review of Image Data Augmentation in Computer Vision
Jisuanji kexue yu tansuo, 2021 Deep learning is a promising solution for computer vision at present. To solve the computer vision problem, it requires massive and high-quality image training datasets.
LIN Chengchuang, SHAN Chun, ZHAO Gansen, YANG Zhirong, PENG Jing, CHEN Shaojie, HUANG Runhua, LI Zhuangwei, YI Xusheng, DU Jiahua, LI Shuangyin, LUO Haoyu, FAN Xiaomao, CHEN Bingchuan
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Frontiers in Biomedical Technologies, 2022 Purpose: 32-time scan duration reduction of 18F-FDG Positron Emission Tomography (PET) images through the generation of standard scan duration images using a multi-slice cycle-consistent Generative Adversarial Network (cycle-GAN) was studied.
Ali Ghafari +4 more
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STAug: Copy-Paste Based Image Augmentation Technique Using Salient Target
IEEE Access, 2022 High-quality, large-capacity data are essential for training a deep learning vision model. However, to construct crop image data, absolute growth time is required for crop growth.
Ji-Soo Kang, Kyungyong Chung
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Distribution-preserving data augmentation [PDF]
PeerJ Computer Science, 2021 In the last decade, deep learning has been applied in a wide range of problems with tremendous success. This success mainly comes from large data availability, increased computational power, and theoretical improvements in the training phase.
Nurdan Ayse Saran +2 more
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Current Directions in Biomedical Engineering, 2021 Data augmentation is a common method to make deep learning assessible on limited data sets. However, classical image augmentation methods result in highly unrealistic images on ultrasound data.
Wulff Daniel +3 more
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Sensors, 2022 Data augmentation is an established technique in computer vision to foster the generalization of training and to deal with low data volume. Most data augmentation and computer vision research are focused on everyday images such as traffic data.
Mingkun Tan +2 more
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Albumentations: Fast and Flexible Image Augmentations
Information, 2020 Data augmentation is a commonly used technique for increasing both the size and the diversity of labeled training sets by leveraging input transformations that preserve corresponding output labels.
Alexander Buslaev +5 more
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