Results 31 to 40 of about 7,122,625 (292)
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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The Effect of Data Augmentation in Deep Learning with Drone Object Detection
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2023 Drone object detection is one of the main applications of image processing technology and pattern recognition using deep learning. However, the limited drone image data that can be accessed for training detection algorithms is a challenge in the ...
Ariel Yonatan Alin +2 more
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A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection. [PDF]
Neural Comput Appl, 2021 Tuberculosis (TB) is known as a potentially dangerous and infectious disease that affects mostly lungs worldwide. The detection and treatment of TB at an early stage are critical for preventing the disease and decreasing the risk of mortality and ...
Tasci E, Uluturk C, Ugur A.
europepmc +2 more sources
Soft Augmentation for Image Classification
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 Modern neural networks are over-parameterized and thus rely on strong regularization such as data augmentation and weight decay to reduce overfitting and improve generalization. The dominant form of data augmentation applies invariant transforms, where the learning target of a sample is invariant to the transform applied to that sample.
Yang Liu +4 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
doaj +1 more source
A Deep Learning Image Data Augmentation Method for Single Tumor Segmentation
Frontiers in Oncology, 2022 PurposeMedical imaging examination is the primary method of diagnosis, treatment, and prevention of cancer. However, the amount of medical image data is often not enough to meet deep learning needs. This article aims to expand the small data set in tumor
Chunling Zhang +6 more
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The use of generative adversarial networks in medical image augmentation
Neural computing & applications (Print), 2023 Generative Adversarial Networks (GANs) have been widely applied in various domains, including medical image analysis. GANs have been utilized in classification and segmentation tasks, aiding in the detection and diagnosis of diseases and disorders ...
A. Makhlouf +3 more
semanticscholar +1 more source
Scientific Reports, 2022 Artificial intelligence (AI) applications in medical imaging continue facing the difficulty in collecting and using large datasets. One method proposed for solving this problem is data augmentation using fictitious images generated by generative ...
Ryo Toda +5 more
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Local Augment: Utilizing Local Bias Property of Convolutional Neural Networks for Data Augmentation
IEEE Access, 2021 Data augmentation is an effective way to increase the diversity of existing training datasets that result in improved generalization ability of convolutional neural networks (CNNs). The augmentation effect is usually global for the existing methods i.e.,
Youmin Kim +2 more
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Image Augmentations for GAN Training
CoRR, 2020 Data augmentations have been widely studied to improve the accuracy and robustness of classifiers. However, the potential of image augmentation in improving GAN models for image synthesis has not been thoroughly investigated in previous studies. In this work, we systematically study the effectiveness of various existing augmentation techniques for GAN ...
Zhengli Zhao +4 more
openaire +2 more sources