Image data augmentation approaches: A comprehensive survey and future directions
Deep learning algorithms have exhibited impressive performance across various computer
vision tasks; however, the challenge of overfitting persists, especially when dealing with …
vision tasks; however, the challenge of overfitting persists, especially when dealing with …
Data augmentation for meta-learning
Conventional image classifiers are trained by randomly sampling mini-batches of images.
To achieve state-of-the-art performance, practitioners use sophisticated data augmentation …
To achieve state-of-the-art performance, practitioners use sophisticated data augmentation …
Detecting novel ototoxins and potentiation of ototoxicity by disease settings
Over 100 drugs and chemicals are associated with permanent hearing loss, tinnitus, and
vestibular deficits, collectively known as ototoxicity. The ototoxic potential of drugs is rarely …
vestibular deficits, collectively known as ototoxicity. The ototoxic potential of drugs is rarely …
Automatic cardiac arrhythmia classification using residual network combined with long short-term memory
Diagnosis and classification of arrhythmia, which is associated with abnormal electrical
activities in the heart, are critical for clinical treatments. Previous studies focused on the …
activities in the heart, are critical for clinical treatments. Previous studies focused on the …
SCL: Self-supervised contrastive learning for few-shot image classification
Few-shot learning aims to train a model with a limited number of base class samples to
classify the novel class samples. However, to attain generalization with a limited number of …
classify the novel class samples. However, to attain generalization with a limited number of …
Deep hierarchical distillation proxy-oil modeling for heterogeneous carbonate reservoirs
This paper presents a novel few-shot proxy modeling approach for the oil and gas industry
to reduce reliance on numerical simulators for reservoir analysis. The strategy introduces a …
to reduce reliance on numerical simulators for reservoir analysis. The strategy introduces a …
Improving automated evaluation of student text responses using gpt-3.5 for text data augmentation
In education, intelligent learning environments allow students to choose how to tackle open-
ended tasks while monitoring performance and behavior, allowing for the creation of …
ended tasks while monitoring performance and behavior, allowing for the creation of …
Towards understanding label smoothing
Label smoothing regularization (LSR) has a great success in training deep neural networks
by stochastic algorithms such as stochastic gradient descent and its variants. However, the …
by stochastic algorithms such as stochastic gradient descent and its variants. However, the …
Recognition of deformation military targets in the complex scenes via MiniSAR submeter images with FASAR-Net
J Lv, D Zhu, Z Geng, S Han, Y Wang… - … on Geoscience and …, 2023 - ieeexplore.ieee.org
Ground-armored weapons have a high detection value in military operations. Satellite
synthetic aperture radar (SAR) cannot accurately detect military targets with meter-level …
synthetic aperture radar (SAR) cannot accurately detect military targets with meter-level …
Keeporiginalaugment: Single image-based better information-preserving data augmentation approach
Advanced image data augmentation techniques play a pivotal role in enhancing the training
of models for diverse computer vision tasks. Notably, SalfMix and KeepAugment have …
of models for diverse computer vision tasks. Notably, SalfMix and KeepAugment have …