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 …

Over the last decade, transfer learning has attracted a great deal of attention as a new
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Deep learning-based models have been shown to outperform human beings in many
computer vision tasks with massive available labeled training data in learning. However …

The focus of recent meta-learning research has been on the development of learning
algorithms that can quickly adapt to test time tasks with limited data and low computational …

Few-shot fine-grained recognition (FS-FGR) aims to distinguish several highly similar
objects from different sub-categories with limited supervision. However, traditional few-shot …

Few-shot object detection is an imperative and long-lasting problem due to the inherent long-
tail distribution of real-world data. Its performance is largely affected by the data scarcity of …

Label smoothing is an effective regularization tool for deep neural networks (DNNs), which
generates soft labels by applying a weighted average between the uniform distribution and …

Recommending cold items in recommendation systems is a longstanding challenge due to
the inherent differences between warm items, which are recommended based on user …

In the field of network security, it is of great significance to accurately detect various types of
Internet of Things (IoT) network intrusion attacks which launched by the attacker-controlled …