Training of binary neural network models using continuous approximation

Dmitrij Pavliuchenkov; Anton Trusov; Elena Limonova

doi:10.1117/12.3023264

3 April 2024 Training of binary neural network models using continuous approximation

Dmitrij Pavliuchenkov, Anton Trusov, Elena Limonova

Proceedings Volume 13072, Sixteenth International Conference on Machine Vision (ICMV 2023); 130720B (2024) https://doi.org/10.1117/12.3023264
Event: Sixteenth International Conference on Machine Vision (ICMV 2023), 2023, Yerevan, Armenia

Abstract

The paper is devoted to the training of binary neural networks. They reduce the requirements for computing power and memory, which is especially important in conditions of limited resources. To date, binary networks do not provide sufficient recognition quality comparable to the quality of traditional floating-point networks, so the development of more efficient methods of training networks are highly relevant. In this paper, we propose a probabilistic model of a neural network that can be transformed into a binary network and consider a way of binarization. Experimental results have shown that our model with incremental binarization and subsequent fine-tuning makes it possible to achieve recognition accuracy of 97.5% for MNIST image classification problem when the accuracy of the binary model trained by Straight Through Estimation was 87.5%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dmitrij Pavliuchenkov, Anton Trusov, and Elena Limonova "Training of binary neural network models using continuous approximation", Proc. SPIE 13072, Sixteenth International Conference on Machine Vision (ICMV 2023), 130720B (3 April 2024); https://doi.org/10.1117/12.3023264

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