In few-shot learning scenarios, the challenge is to generalize and perform well on new
unseen examples when only very few labeled examples are available for each task. Model …

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

Meta-learning offers an effective solution to learn new concepts under scarce supervision
through an episodic-training scheme: a series of target-like tasks sampled from base classes …

Understanding the structure of multiple related tasks allows for multi-task learning to improve
the generalisation ability of one or all of them. However, it usually requires training each …

To benefit the learning of a new task, meta-learning has been proposed to transfer a well-
generalized meta-model learned from various meta-training tasks. Existing meta-learning …

Few-shot learning (FSL) aims to learn novel tasks with very few labeled samples by
leveraging experience from\emph {related} training tasks. In this paper, we try to understand …

The goal of data-free meta-learning is to learn useful prior knowledge from a collection of
pre-trained models without accessing their training data. However, existing works only solve …

Data-free meta-learning (DFML) aims to enable efficient learning of new tasks by meta-
learning from a collection of pre-trained models without access to the training data. Existing …

Abstract Data-Free Meta-Learning (DFML) aims to extract knowledge from a collection of pre-
trained models without requiring the original data presenting practical benefits in contexts …

Considering the changing working conditions of rotating machinery in operation, it is often
difficult to collect data accurately in some severe fault states, and the lack of data can lead to …