Results 51 to 60 of about 7,122,625 (292)
Small facial image dataset augmentation using conditional GANs based on incomplete edge feature input [PDF]
PeerJ Computer Science, 2021 Image data collection and labelling is costly or difficult in many real applications. Generating diverse and controllable images using conditional generative adversarial networks (GANs) for data augmentation from a small dataset is promising but ...
Shih-Kai Hung, John Q. Gan
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Journal of Electrical Systems and Information Technology, 2023 The exponential growth of deep learning networks has enabled us to handle difficult tasks, even in the complex field of medicine. Nevertheless, for these models to be extremely generalizable and perform well, they need to be applied to a vast corpus of ...
Peshraw Ahmed Abdalla +2 more
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Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review [PDF]
arXiv.org, 2022 In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e.g., image classification, segmentation, object detection and localization), in the presence of challenges with biological ...
E. Olaniyi +3 more
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RSMDA: Random Slices Mixing Data Augmentation
Applied Sciences, 2023 Advanced data augmentation techniques have demonstrated great success in deep learning algorithms. Among these techniques, single-image-based data augmentation (SIBDA), in which a single image’s regions are randomly erased in different ways, has shown ...
Teerath Kumar +3 more
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ACM Transactions on Graphics, 2016 As humans, we regularly interpret scenes based on how objects arerelated, rather than based on the objects themselves. For example, we see a personridingan object X or a plankbridgingtwo objects. Current methods provide limited support to search for content based on such relations.
Paul Guerrero 0001 +2 more
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De Computis, 2023 This research paper presents a deep-learning approach to early detection of skin cancer using image augmentation techniques. We introduce a two-stage image augmentation process utilizing geometric augmentation and a generative adversarial network (GAN ...
Catur Supriyanto +3 more
semanticscholar +1 more source
An Efficient Data Augmentation Network for Out-of-Distribution Image Detection
IEEE Access, 2021 Since deep neural networks may classify out-of-distribution image data into in-distribution classes with high confidence scores, this problem may cause serious or even fatal hazards in certain applications, such as autonomous vehicles and medical ...
Cheng-Hung Lin +3 more
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Journal of Applied Data Sciences, 2023 This study aims to apply Convolutional Neural Networks (CNN) and image augmentation techniques in digit recognition using the MNIST dataset. We built a CNN model and experimented with various image augmentation techniques to improve digit recognition ...
Khodijah Hulliyah
semanticscholar +1 more source
Deep Learning-Based Plant-Image Classification Using a Small Training Dataset
Mathematics, 2022 Extensive research has been conducted on image augmentation, segmentation, detection, and classification based on plant images. Specifically, previous studies on plant image classification have used various plant datasets (fruits, vegetables, flowers ...
Ganbayar Batchuluun +2 more
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DATA AUGMENTATION APPROACHES FOR SATELLITE IMAGE SUPER-RESOLUTION [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 Data augmentation is a well known technique that is frequently used in machine learning tasks to increase the number of training instances and hence decrease model over-fitting.
M. A. A. Ghaffar +3 more
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