March 15, 2018

Convolutional Neural Network Structure Transformations for Complexity Reduction and Speed Improvement

Pattern Recognition and Image Analysis – 2018. – Vol. 28(1). – P. 24-33. – DOI: 10.1134/S105466181801011X.

Two methods of convolution-complexity reduction, and therefore acceleration of convolutional neural network processing, are introduced. Convolutional neural networks (CNNs) are widely used in computer vision problems. In the first method, we propose to change the structure of the convolutional layer of the neural network into a separable one, which is more computationally simple. It is shown experimentally that the proposed structure makes it possible to achieve up to a 5.6-fold increase in the operating speed of the convolutional layer for 11 × 11-sized convolutional filters without loss in recognition accuracy. The second method uses 1 × 1 fusing convolutions to increase the number of convolution outputs along with decreasing the number of filters. It decreases the computational complexity of convolution and provides an experimental processing speed increase of 11% in the case of large convolutional filters. It is shown that both proposed methods preserve accuracy when tested with the recognition of Russian letters, CIFAR-10, and MNIST images.

results.link.springer.com

Test Drive Our Smart Engines

Free demo apps allow you to experience the power of Smart Engines software for intelligent document scanning in a real-world context.

Why not experience the power of Smart Engines for yourself? Our demo apps allow you to test the capabilities of our identity document recognition software on mobile devices in videostream or in a single image (photo, scan).

Simply display any document to the camera in real-time or choose a photo from the gallery, and the app will recognize and capture the necessary data.

Demo apps Privacy Policy

id documents enginge by Smart Engines
Apple App Store Badge
Google Play Badge
id documents enginge by Smart Engines

Get in Touch

For questions about our products, research, people or project proposals, please get in touch.

Contact Form
Warning before submitting your request:

Smart Engines is fully committed to provide an answer within 2 working days. However, it is your responsibility that your IT infrastructure does not block our reply or redirect it into your spams. If you haven’t received any answer from us within 2 working days, please check your spams or simply call us.

Smart Engines guarantees that the provided information will not be made public and will be used only internally.