The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

Few-shot learning (FSL) is popular due to its ability to adapt to novel classes. Compared
with inductive few-shot learning, transductive models typically perform better as they …

Few-shot learning (FSL) has received a lot of attention due to its remarkable ability to adapt
to novel classes. Although many techniques have been proposed for FSL, they mostly focus …

Few-shot learning amounts to learning representations and acquiring knowledge such that
novel tasks may be solved with both supervision and data being limited. Improved …

In practical settings, a speaker recognition system needs to identify a speaker given a short
utterance, while the enrollment utterance may be relatively long. However, existing speaker …

Few-shot classification (FSC) is a challenging problem, which aims to identify novel classes
with limited samples. Most existing methods employ vanilla transfer learning or episodic …

Few-shot visual recognition refers to recognize novel visual concepts from a few labeled
instances. Many few-shot visual recognition methods adopt the metric-based meta-learning …

Automated visual inspection has the potential to improve the efficiency and accuracy of
inspection tasks across various industries. Deep learning models have been at the forefront …

Self-training emerges as an important research line on domain adaptation. By taking the
model's prediction as the pseudo labels of the unlabeled data, self-training bootstraps the …

Few-shot learning investigates how to solve novel tasks given limited labeled data.
Exploiting unlabeled data along with the limited labeled has shown substantial improvement …