Learning information from a single or a few samples is called few-shot learning. This
learning method will solve deep learning's dependence on a large sample. Deep learning …

Few-shot learning in image classification is developed to learn a model that aims to identify
unseen classes with only few training samples for each class. Fewer training samples and …

Conventional image classification methods usually require a large number of training
samples for the training model. However, in practical scenarios, the amount of available …

Deep neural networks can learn a huge function space, because they have millions of
parameters to fit large amounts of labeled data. However, this advantage is a major obstacle …

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 …

The rapid development of deep learning provides great convenience for production and life.
However, the massive labels required for training models limits further development. Few …

Few-shot classification aims to recognize unlabeled samples from unseen classes given
only few labeled samples. The unseen classes and low-data problem make few-shot …

Few-shot image classification is a challenging problem that aims to achieve the human level
of recognition based only on a small number of training images. One main solution to few …

The goal of few-shot learning is to recognize new visual concepts with just a few amount of
labeled samples in each class. Recent effective metric-based few-shot approaches employ …

The focus of recent few-shot learning research has been on the development of learning
methods that can quickly adapt to unseen tasks with small amounts of data and low …