Review of convolutional neural network optimization and training in image processing

Yong Ren; Xuemin Cheng

doi:10.1117/12.2512087

7 March 2019 Review of convolutional neural network optimization and training in image processing

Yong Ren, Xuemin Cheng

Proceedings Volume 11053, Tenth International Symposium on Precision Engineering Measurements and Instrumentation; 1105331 (2019) https://doi.org/10.1117/12.2512087
Event: 10th International Symposium on Precision Engineering Measurements and Instrumentation (ISPEMI 2018), 2018, Kunming, China

Abstract

Major breakthroughs in the fields of image processing, pattern recognition, and scene classification have recently been made through the using of convolutional neural networks (CNNs) and deep learning. If the training set is sufficient, a CNN performs better than traditional machine learning algorithms and differs from them in many ways. In CNNs, feature extraction is more intelligent, so researchers no longer require extensive knowledge about the specific topic. They can focus on the CNN itself, including the structural design of the network, the model optimization, and the numerical solution. The many articles on CNNs published over the past few years propose many neural network models. Methods of training and optimization for CNNs have also been proposed. This paper reviews the history of CNNs, introduces the commonly established CNN structure, and summarizes methods and tips for CNN training.

Citation Download Citation

Yong Ren and Xuemin Cheng "Review of convolutional neural network optimization and training in image processing", Proc. SPIE 11053, Tenth International Symposium on Precision Engineering Measurements and Instrumentation, 1105331 (7 March 2019); https://doi.org/10.1117/12.2512087

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