Meta-learning empowers learning systems with the ability to acquire knowledge from
multiple tasks, enabling faster adaptation and generalization to new tasks. This review …

Distance metric learning is a branch of machine learning that aims to learn distances from
the data, which enhances the performance of similarity-based algorithms. This tutorial …

The real-world large industry has gradually become a data-rich environment with the
development of information and sensor technology, making the technology of data-driven …

Few-shot learning aims to learn a well-performing model from a few labeled examples.
Recently, quite a few works propose to learn a predictor to directly generate model …

Few-shot action recognition aims to recognize novel action classes (query) using just a few
samples (support). The majority of current approaches follow the metric learning paradigm …

Defect classification exhibits great importance in metal surface defect inspection. Most
previous defect classification models are based on fully supervised learning, which requires …

State-of-the-art deep learning systems (eg, ImageNet image classification) typically require
very large training sets to achieve high accuracies. Therefore, one of the grand challenges is …

Pattern recognition has made great progress under large amount of labeled data, while
performs poorly on a very few examples obtained, named few-shot classification, where a …

Deep learning is an effective method for extracting the underlying information in text.
However, it performs better on closed datasets and is less effective in real-world scenarios …

As artificial intelligence (AI) has already seen numerous successful applications, the
upcoming challenge lies in how to realize artificial general intelligence (AGI). Self-learning …