A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities
Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …
Towards open-set touchless palmprint recognition via weight-based meta metric learning
Touchless biometrics has become significant in the wake of novel coronavirus 2019 (COVID-
19). Due to the convenience, user-friendly, and high-accuracy, touchless palmprint …
19). Due to the convenience, user-friendly, and high-accuracy, touchless palmprint …
Multimodal triplet attention network for brain disease diagnosis
Multi-modal imaging data fusion has attracted much attention in medical data analysis
because it can provide complementary information for more accurate analysis. Integrating …
because it can provide complementary information for more accurate analysis. Integrating …
Double-cohesion learning based multiview and discriminant palmprint recognition
Palmprint recognition has been widely used in security authentication. However, most of the
existing palmprint representation methods are focused on a special application scenario …
existing palmprint representation methods are focused on a special application scenario …
Privacy preserving palmprint recognition via federated metric learning
Deep learning-based palmprint recognition methods have made good progress and
obtained promising performance. However, most of them are mainly focused on …
obtained promising performance. However, most of them are mainly focused on …
Multilevel noise contrastive network for few-shot image denoising
In recent years, most denoising methods based on deep convolutional neural networks
heavily rely on massive noisy–clean image pairs. Collecting massive noisy–clean image …
heavily rely on massive noisy–clean image pairs. Collecting massive noisy–clean image …
Few-shot fault diagnosis method of rotating machinery using novel MCGM based CNN
G Yu, P Wu, Z Lv, J Hou, B Ma… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The existing fault diagnosis methods can achieve good results when various status fault
data are available. However, the construction of the diagnosis model is often unachievable …
data are available. However, the construction of the diagnosis model is often unachievable …
Metaemotionnet: spatial-spectral-temporal based attention 3D dense network with meta-learning for EEG emotion recognition
Emotion recognition has become an important area in affective computing. Emotion
recognition based on multichannel electroencephalogram (EEG) signals has gradually …
recognition based on multichannel electroencephalogram (EEG) signals has gradually …
Multiple scale convolutional few-shot learning networks for online P300-based brain–computer interface and its application to patients with disorder of consciousness
P300 brain–computer interfaces (BCIs) have significant potential for detecting and
assessing residual consciousness in patients with disorders of consciousness (DoC) but are …
assessing residual consciousness in patients with disorders of consciousness (DoC) but are …
Detail R-CNN: Insulator detection based on detail feature enhancement and metric learning
F Shuang, S Wei, Y Li, X Gu, Z Lu - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Insulators need to be regularly inspected to ensure the normal operation of the power
system. Currently, the detection of insulators in power distribution networks poses greater …
system. Currently, the detection of insulators in power distribution networks poses greater …