In machine learning and computer vision fields, due to the rapid development of deep learning, recent years have witnessed breakthroughs for large-sample classification tasks. However, it remains a persistent challenge to learn a deep neural network with good generalizability from only a small number of training samples. In fact, humans can easily learn the concept of a class from a small amount of data rather than from millions of data. Moreover, many types of real-world data are small in quantity and are expensive to collect and label. Motivated by this fact, research on deep learning with small samples becomes more and more prevalent in the communities of machine learning and computer vision, for example, researches focusing on one-shot classification, few-shot classification, as well as classification with small training samples.
Recently, deep small-sample learning has achieved promising performance in certain small-sample problems, by transferring the "Knowledge" learned from other datasets containing rich labelled data or generating synthetic samples to approximate the distribution of real data. However, many challenging topics remain with small-sample deep leaning techniques, such as data augmentation, feature learning, prior construction, meta-learning, fine tuning, etc. Therefore, the goal of this special issue is to collect and publish the latest developments in various aspects of deep learning with small samples.
The list of possible topics includes, but is not limited to:
Survey/vision/review of deep learning with small samples
Data augmentation methods for small-sample leaning
Feature learning based methods small-sample leaning
Regularization technology of deep model in small-sample leaning
Ensemble learning based methods for small-sample learning
Transfer learning methods for small-sample learning
Semi-supervised learning methods for small-sample learning
Prior based methods for few-shot learning
Meta-leaning based methods for few-shot learning
Fine-tuning based methods for small-sample learning
Theoretical analysis for small-sample learning
Applications of small-sample learning on person re-identification, object recognition, etc.
