Journal of Guangdong University of Technology ›› 2022, Vol. 39 ›› Issue (05): 1-8.doi: 10.12052/gdutxb.220092

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

Zhang Yun, Wang Xiao-dong   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2022-05-13 Online:2022-09-10 Published:2022-07-18

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Abstract: 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".

Key words: deep learning method, convolutional neural network, restricted sample learning, functional evolution

CLC Number: 

  • TP183
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