Machine learning has been highly successful in data-intensive applications but is often
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …

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 …

Few-Shot Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …

Few-shot learning, especially few-shot image classification, has received increasing
attention and witnessed significant advances in recent years. Some recent studies implicitly …

We propose a transductive Laplacian-regularized inference for few-shot tasks. Given any
feature embedding learned from the base classes, we minimize a quadratic binary …

Both few-shot and continual learning have seen substantial progress in the last years due to
the introduction of proper benchmarks. That being said, the field has still to frame a suite of …

Transductive inference is widely used in few-shot learning, as it leverages the statistics of
the unlabeled query set of a few-shot task, typically yielding substantially better …

Building a good feature space is essential for the metric-based few-shot algorithms to
recognize a novel class with only a few samples. The feature space is often built by …

In many real-life problems, it is difficult to acquire or label large amounts of data, resulting in
so-called few-shot learning problems. However, few-shot classification is a challenging …

Few-shot classification is the task of predicting the category of an example from a set of few
labeled examples. The number of labeled examples per category is called the number of …