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Deep neural networks performance optimization in image recognition
P. 649–654.
A. G. Rassadin, A. V. Savchenko
In this paper, we consider the problem of insufficient runtime and memory-space complexities of contemporary deep convolutional neural networks in the problem of image recognition. A survey of recent compression methods and efficient neural networks architectures is provided. The experimental study is focused on the visual emotion recognition problem. We compare the computational speed and memory consumption during the training and the inference stages of such methods as the weights matrix decomposition, binarization and hashing in the visual emotion recognition problem. It is experimentally shown that the most efficient recognition is achieved with the full network binarization and matrices decomposition.
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Самара: Новая техника, 2017.
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Savchenko A., Information Sciences 2021 Vol. 560 P. 370–385
A novel image recognition algorithm based on sequential three-way decisions is introduced to speed up the inference in a convolutional neural network. In contrast to the majority of existing studies, our approach does not require a special procedure to train a neural network, and thus it can be used with arbitrary architectures including pre-trained convolutional ...
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In this paper fast image recognition techniques based on statistical sequential analysis are discussed. We examine the possibility to sequentially process the principal components and organize a convolutional neural net- work with early exits. Particular attention is paid to sequentially learn multi-task lightweight neural network model to predict several facial at- tributes (age, gender and ...
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Text encodings from automatic speech recognition (ASR) transcripts and audio representations have shown promise in speech emotion recognition (SER) ever since. Yet, it is challenging to explain the effect of each information stream on the SER systems. Further, more clarification is required for analysing the impact of ASR's word error rate (WER) on linguistic emotion ...
Added: November 17, 2020
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In this paper the problem of high computational complexity of deep convolutional nets in image recognition is considered. An existing framework of adaptive neural networks is extended by appending the separate classifier to intermediate layers. The hierarchical representations of the input image are sequentially analyzed. If the first classifier returns rather high confidence score, the ...
Added: October 15, 2020
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In this paper, we address the problem of detecting small objects on high-quality X-ray imagesusing deep neural networks. We propose to implement the two-stage approach, in which, firstly, input image issplit into partially overlapping blocks to make small objects more discriminative for detection. Secondly, the small blocks are fed into conventional single-shot detectors. These detectors ...
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Savchenko A., IEEE Transactions on Neural Networks and Learning Systems 2020 Vol. 31 No. 2 P. 651–660
If the training data set in image recognition task is not very large, the feature extraction with a convolutional neural network is usually applied. Here, we focus on the nonparametric classification of extracted feature vectors using the probabilistic neural network (PNN). The latter is characterized by the high runtime and memory space complexity. We propose ...
Added: November 1, 2019
Kopeykina Lyudmila, Savchenko A., , in: 2019 International Russian Automation Conference (RusAutoCon).: IEEE, 2019. P. 1–6.
The authors consider the problem of automatic detection of private scanned documents based on text recognition with deep neural networks. The paper suggests implementing a two-phase approach with the first stage which includes efficient EAST text detection and recognition using Tesseract OCR Engine. Secondly, the authors classify the privacy of a scanned document by deep ...
Added: October 21, 2019
Sokolov A., Savchenko A., , in: 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI).: IEEE, 2019. Ch. 19 P. 113–116.
In this article, we focus on the isolated voice command recognition for autonomous man-machine and intelligent robotic systems. We propose to create a grammar model for a small testing command set with self-loops for each state to return blank symbols for noise and out-of-vocabulary words. In addition, we use single arc connected beginning and ending ...
Added: October 21, 2019
Berlin: Springer, 2019.
This two-volume set LNCS 10305 and LNCS 10306 constitutes the refereed proceedings of the 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, held at Gran Canaria, Spain, in June 2019.
The 150 revised full papers presented in this two-volume set were carefully reviewed and selected from 210 submissions. The papers are organized in topical sections ...
Added: July 29, 2019