The main learning strategy of the PCANet is using Principal Component Analysis (PCA) for
learning the convolutional filters from the data. The assumption that all the image patches …

Nowadays many deep learning algorithms have been employed to solve different types of
problems in the area of computer vision; however, most of them require a great amount of …

Deep learning has shown remarkable performance in quantity of vision tasks. However, its
large network generally requires quantity of samples to support sufficient parameters …

Prototype-based few-shot learning methods are promising in that they are simple yet
effective to handle any-shot problems, and many prototype associated works are raised …

Few-shot classification aims to adapt classifiers trained on base classes to novel classes
with a few shots. However, the limited amount of training data is often inadequate to …

Few-shot learning (FSL) remains a challenging research endeavor. Traditional few-shot
learning methods mainly consider the distance relationship between the query set and the …

Few-shot learning aims to train a model with a limited number of base class samples to
classify the novel class samples. However, to attain generalization with a limited number of …

Few-shot classification aims to adapt classifiers to novel classes with a few training samples.
However, the insufficiency of training data may cause a biased estimation of feature …

Few-shot learning is a challenging task because it focuses on classifying new object
categories given only limited labeled samples and often results in poor generalization. Most …

This paper proposes a multi-layer neural network structure for few-shot image recognition of
novel categories. The proposed multi-layer neural network architecture encodes …