logo
  • 主页
  • 最近更新
  • 关于我们
人工智能

Neurocomputing

Special Issue on Deep Learning with Small Samples

摘要截稿:
全文截稿: 2020-04-15
影响因子: 3.241
期刊难度:
CCF分类: C类
中科院JCR分区:
  • 大类 : 工程技术 - 2区
  • 小类 : 计算机:人工智能 - 2区
Overview
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.