The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …

Facial expression recognition (FER) is embedded in many real-world human-computer
interaction tasks, such as online learning, depression recognition and remote diagnosis …

Few-shot learning aims to learn a classifier with more generalization capability from
extremely limited labeled samples has drawn an increasing amount of attention in many …

The success of fault diagnosis based on deep learning benefits from a large amount of
labeled fault samples. However, the scarcity of labeled fault samples in fault diagnosis of …

Semi-supervised learning (SSL) is effective in addressing the scarcity of label information in
the fault diagnosis of rotating machinery. However, existing SSL methods generally assume …

Few-shot classification addresses the challenge of swiftly enabling a deep learning model to
comprehend new classes based on minimal supporting image samples. Despite recent …

Few-shot action recognition aims to learn novel action classes with only a few annotated
samples. This is a challenging problem because motion modeling is difficult, especially …

Few-shot learning in visual classification aims to emulate human capacity to recognize
unseen classes using only a few samples. Prototype-based methods have shown promising …

Few-shot learning aims to learn classification with only a few labeled samples. Prototypical
network has become the focus of few-shot learning, but the prototypical network has some …

为了解决机器学**在样本量较少的情况下所面临的巨大挑战, 研究人员提出了小样本学**的概念
. 在现有的小样本学**研究工作中, 嵌入学**方法取得了不错的效果, 引发了大量关注 …