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Environment Adaptive Deep Learning Classification System Based on One-shot Guidance

Guanghao Jin1, Chunmei Pei1, Na Zhao1, Hengguang Li2, Qingzeng Song3, Jing Yu1,*

1 School of Telecommunication Engineering, Beijing Polytechnic, Beijing, 100176, China
2 Department of Mathematics, Wayne State University, Detroit, 48202, MI, USA
3 School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China

* Corresponding Author: Jing Yu. Email:

Computers, Materials & Continua 2022, 73(3), 5185-5196.


When utilizing the deep learning models in some real applications, the distribution of the labels in the environment can be used to increase the accuracy. Generally, to compute this distribution, there should be the validation set that is labeled by the ground truths. On the other side, the dependency of ground truths limits the utilization of the distribution in various environments. In this paper, we carried out a novel system for the deep learning-based classification to solve this problem. Firstly, our system only uses one validation set with ground truths to compute some hyper parameters, which is named as one-shot guidance. Secondly, in an environment, our system builds the validation set and labels this by the prediction results, which does not need any guidance by the ground truths. Thirdly, the computed distribution of labels by the validation set selectively cooperates with the probability of labels by the output of models, which is to increase the accuracy of predict results on testing samples. We selected six popular deep learning models on three real datasets for the evaluation. The experimental results show that our system can achieve higher accuracy than state-of-art methods while reducing the dependency of labeled validation set.


Cite This Article

G. Jin, C. Pei, N. Zhao, H. Li, Q. Song et al., "Environment adaptive deep learning classification system based on one-shot guidance," Computers, Materials & Continua, vol. 73, no.3, pp. 5185–5196, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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