Results 1 to 10 of about 7,122,625 (292)
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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Albumentations: Fast and Flexible Image Augmentations [PDF]
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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Polarimetric image augmentation [PDF]
2020 25th International Conference on Pattern Recognition (ICPR), 2021 7 pages, submitted to ICPR2020 second ...
Blanchon, Marc +4 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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Image Augmentation for Satellite Images
CoRR, 2022 This study proposes the use of generative models (GANs) for augmenting the EuroSAT dataset for the Land Use and Land Cover (LULC) Classification task. We used DCGAN and WGAN-GP to generate images for each class in the dataset. We then explored the effect of augmenting the original dataset by about 10% in each case on model performance.
Oluwadara Adedeji +3 more
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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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Retrieval-augmented Image Captioning
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023 Inspired by retrieval-augmented language generation and pretrained Vision and Language (V&L) encoders, we present a new approach to image captioning that generates sentences given the input image and a set of captions retrieved from a datastore, as opposed to the image alone.
Ramos, Rita +2 more
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Understanding the Benefits of Image Augmentations
CoRR, 2023 Image Augmentations are widely used to reduce overfitting in neural networks. However, the explainability of their benefits largely remains a mystery. We study which layers of residual neural networks (ResNets) are most affected by augmentations using Centered Kernel Alignment (CKA). We do so by analyzing models of varying widths and depths, as well as
Matthew Iceland, Christopher Kanan
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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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