Image augmentation techniques for convolutional neural network [PDF]
, 2022 openUna delle più grandi sfide per le Reti Neurali Convoluzionali, soprattutto ora che vengono utilizzate ampiamente in svariati contesti, è la mancanza di training set adeguati per sessioni di training robuste e meno prone ad overfitting.
BRAVIN, RICCARDO
core
Data Augmentation Techniques for Deep Learning-Based Medical Image Analyses
대한영상의학회지, 2020 Medical image analyses have been widely used to differentiate normal and abnormal cases, detect lesions, segment organs, etc. Recently, owing to many breakthroughs in artificial intelligence techniques, medical image analyses based on deep learning ...
Mingyu Kim, Hyun-Jin Bae
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ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION
Applied Computer Science, 2023 Soil is a solid-particle that covers the earth's surface. Soils can be classified based their color. The color can be an indication of soil properties and soil conditions. Soil image classification requires high accuracy and caution.
Sri INDRA MAIYANTI +6 more
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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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Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation
Algorithms, 2022 Data augmentation methods enrich datasets with augmented data to improve the performance of neural networks. Recently, automated data augmentation methods have emerged, which automatically design augmentation strategies.
Misgana Negassi +2 more
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Effects of data count and image scaling on Deep Learning training [PDF]
PeerJ Computer Science, 2020 Background Deep learning using convolutional neural networks (CNN) has achieved significant results in various fields that use images. Deep learning can automatically extract features from data, and CNN extracts image features by convolution processing ...
Daisuke Hirahara +3 more
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Sagtta: saliency guided test time augmentation for medical image segmentation across vendor domain shift [PDF]
, 2023 Test time augmentation has been shown to be an effective approach to combat domain shifts in deep learning. Despite their promising performance levels, the interpretability of the underlying used models is however low. Saliency maps have been widely used
Tomar, Devavrat +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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Data augmentation approaches for polyp segmentation [PDF]
, 2022 openImage augmentation, and in general data augmentation techniques, can greatly improve the performances of deep neural networks through the creation of artificial patterns, in fact the presence of these new patterns helps the network to generalise thus
DORIZZA, ALBERTO
core
Effectiveness of image augmentation operations.
, 2022 N/A refers to taking no image augmentation method. OS and BS are short for object swap and layer swap, others refer to randomly selected image augmentation methods other than the two newly designed operations.
Fan Wang (135182) +5 more
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