基于受限样本的深度学习综述与思考

深度学习目前依靠大数据和强算力取得了较大进展，但在样本受限情况下的表现差强人意，主要问题在于函数空间(簇)的建构和在数据集受限情况下算法的设计。据此，本文对受限样本下的深度学习进行了分类综述。另外，从目前对大脑的研究来看，人的认知过程在大脑中是分区域的，每个区域担负的功能是不同的，对每个区域功能的学习过程也应该是有差异的。因此，提出了“功能进阶”式的深度学习的设想，试图构建分区分层多种功能模块组成的网络结构，研究“进阶”式的功能模块训练方法，以期探求“仿人学习”的新路径。

A Review and Thinking of Deep Learning with a Restricted Number of Samples

Deep learning has achieved great success with big data and powerful computing, but its performance is poor under sample constraint, mainly due to the construction of function space (clusters) and the design of algorithms under dataset constraint. Accordingly, a categorical review of deep learning under restricted samples is presented. In addition, according to the current research on the brain, the cognitive process of humankind is categorized in the brain with different regions, and the cognitive functions of each region are also different. Therefore, the training function of each region should also be different. At this point, an idea of deep learning method using functional evolution is proposed, trying to create a network structure composed of multiple functional modules, and the training procedure of the functional module used in this method is studied, aiming to explore the new area of "humanoid learning".