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of 49
pro vyhledávání: '"Limonova, Elena"'
Low-bit quantized neural networks are of great interest in practical applications because they significantly reduce the consumption of both memory and computational resources. Binary neural networks are memory and computationally efficient as they re
Externí odkaz:
http://arxiv.org/abs/2205.09120
Autor:
Trusov, Anton1,2,3 (AUTHOR) dimonstr@iitp.ru, Limonova, Elena1,2 (AUTHOR) vva@smartengines.com, Nikolaev, Dmitry2,4 (AUTHOR), Arlazarov, Vladimir V.1,2 (AUTHOR)
Publikováno v:
Mathematics (2227-7390). Mar2024, Vol. 12 Issue 5, p651. 22p.
One of the most computationally intensive parts in modern recognition systems is an inference of deep neural networks that are used for image classification, segmentation, enhancement, and recognition. The growing popularity of edge computing makes u
Externí odkaz:
http://arxiv.org/abs/2009.07190
Quantized low-precision neural networks are very popular because they require less computational resources for inference and can provide high performance, which is vital for real-time and embedded recognition systems. However, their advantages are ap
Externí odkaz:
http://arxiv.org/abs/2009.06488
Publikováno v:
International Journal of Applied Engineering Research (ISSN 0973-4562), Volume 11, Number 24 (2016), pp. 11675-11680
In this paper we consider speedup potential of morphological image filtering on ARM processors. Morphological operations are widely used in image analysis and recognition and their speedup in some cases can significantly reduce overall execution time
Externí odkaz:
http://arxiv.org/abs/2002.09474
Publikováno v:
International Journal of Applied Engineering Research (ISSN 0973-4562), Volume 11, Number 11 (2016), pp 7491-7494
This paper considers a convolutional neural network transformation that reduces computation complexity and thus speedups neural network processing. Usage of convolutional neural networks (CNN) is the standard approach to image recognition despite the
Externí odkaz:
http://arxiv.org/abs/2002.07754
Autor:
Trusov, Anton, Limonova, Elena
In this work we apply commonly known methods of non-adaptive interpolation (nearest pixel, bilinear, B-spline, bicubic, Hermite spline) and sampling (point sampling, supersampling, mip-map pre-filtering, rip-map pre-filtering and FAST) to the problem
Externí odkaz:
http://arxiv.org/abs/1912.01401
In the paper we introduce a novel bipolar morphological neuron and bipolar morphological layer models. The models use only such operations as addition, subtraction and maximum inside the neuron and exponent and logarithm as activation functions for t
Externí odkaz:
http://arxiv.org/abs/1911.01971
Akademický článek
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Publikováno v:
Proceedings of SPIE; April 2024, Vol. 13072 Issue: 1 p130720B-130720B-8, 1176489p