Synchronous Deep Reinforcement Learning (SDRL) Algorithm For Small Batch Image Recognition

In order to get a good image recognition model, a lot of high-quality data must be trained. However, some special fields (such as battlefield, rare medical images, etc.) have the characteristics of difficult to obtain data and difficult to label. In order to get a small batch image recognition model...

Full description

Saved in:
Bibliographic Details
Published in2022 8th International Conference on Big Data and Information Analytics (BigDIA) pp. 317 - 323
Main Authors Juan, Yang, Chaoqing, Xiao, Yize, Zheng, Feifan, Shen
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.08.2022
Subjects
Classification algorithms
Computational modeling
Convolutional neural networks
Cross entropy
Data models
Deep learning
Image recognition
Reinforcement learning
Strategy function
Target recognition
Online AccessGet full text

Cover

More Information
Summary:In order to get a good image recognition model, a lot of high-quality data must be trained. However, some special fields (such as battlefield, rare medical images, etc.) have the characteristics of difficult to obtain data and difficult to label. In order to get a small batch image recognition model with good performance under the condition of insufficient data, a Synchronous Deep Reinforcement Learning algorithm for small-batch image recognition is proposed, and the algorithm is used to recognize image data such as combat elements. The algorithm is based on reinforcement learning and convolutional neural network and so on, it is obtained by optimizing it. Through experimental analysis, the algorithm can quickly realize small batch image classification, and the accuracy rate is higher than other similar algorithms, which proves the practicability and feasibility of this algorithm.
ISSN:2771-6902
DOI:10.1109/BigDIA56350.2022.9874163