Персона: Трофимов, Александр Геннадьевич
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Институт интеллектуальных кибернетических систем
Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.
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Александр Геннадьевич
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- ПубликацияТолько метаданныеA method of choosing a pre-trained convolutional neural network for transfer learning in image classification problems(2020) Trofimov, A. G.; Bogatyreva, A. A.; Трофимов, Александр Геннадьевич© Springer Nature Switzerland AG 2020.A method of choosing a pre-trained convolutional neural network (CNN) for transfer learning on the new image classification problem is proposed. The method can be used for quick estimation of which of the CNNs trained on the ImageNet dataset images (AlexNet, VGG16, VGG19, GoogLeNet, etc.) will be the most accurate after its fine tuning on the new sample of images. It is shown that there is high correlation (ρ ≈ 0.74, p < 0.01) between the characteristics of the features obtained at the output of the pre-trained CNN’s convolutional part and its accuracy on the test sample after fine tuning. The proposed method can be used to make recommendations for researchers who want to apply the pre-trained CNN and transfer learning approach to solve their own classification problems and don’t have sufficient computational resources and time for multiple fine tunings of available free CNNs with consequent choosing the best one.
- ПубликацияТолько метаданныеPredictive Model for Calculating Abnormal Functioning Power Equipment(2020) Korshikova, A. A.; Trofimov, A. G.; Трофимов, Александр Геннадьевич© 2020, Springer Nature Switzerland AG.A method of early detection of defects in technological equipment of energy facilities is proposed. A brief analysis of the Russian market of cyber-physical industrial equipment monitoring systems was carried out. Special attention is paid to the problems of preparing initial data for training a model, in particular, the problem of obtaining adequate data on accidents that have occurred. A mathematical problem is formulated for modeling the anomaly index, which takes values from 0 (normal operation) to 1 (high probability of an accident). The model is based on well-known statistical methods. A method for dividing the periods of operation of technological equipment into “normal” and “anomalous” is proposed. The method of binary classification AUC ROC allows you to limit the number of signs involved in the formation of the anomaly indicator, signs that have a good “separation” ability. Using the Spearman’s rank correlation criterion, signs are selected that are most sensitive to the development of process equipment malfunctions. As an anomalous indicator, it is proposed to consider the ratio of the densities of distribution of the final signs, estimated in the anomalous and normal areas of operation of the process equipment. A method is proposed for generating an alarm for detecting the anomalous operation of the technological equipment of power units. It is shown that the proposed model made it possible to identify the beginning of the development of an emergency, while individual measurements did not detect any features of the operation of equipment of energy facilities in the pre-emergency time interval.
- ПубликацияТолько метаданныеCross-Modal Transfer Learning for Image and Sound(2022) Soroka, A. A.; Trofimov, A. G.; Сорока, Артем Александрович; Трофимов, Александр Геннадьевич© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Recently the research on transfer learning between similar domains has become increasingly common. However, the fields of cross-domain and cross-modal knowledge transfers are more complicated and have been studied less. We propose the new transfer learning strategy between tasks on essentially different domains called as cross-modal transfer learning and consider its ideas and the algorithm. The key element of cross-modal transfer pipeline is cross-modal adapter, i.e. a neural network that transforms the target domain features to the source domain features that can be efficiently processed by a pre-trained neural network. In the experiments the dataset ImageNet and audio dataset ESC-50 are chosen as source domain and target domain respectively. It is shown that a fairly simple neural cross-modal adapter makes it possible to achieve high classification accuracy on target domain using the knowledge obtained by pre-trained neural network on the source domain. Our experiments also show that cross-modal transfer learning noticeably reduces the training time in comparison with the building target model “from scratch”.
- ПубликацияТолько метаданныеRussian Language Speech Generation from Facial Video Recordings Using Variational Autoencoder(2023) Leonov, M. M.; Soroka, A. A.; Trofimov, A. G.; Сорока, Артем Александрович; Трофимов, Александр Геннадьевич
- ПубликацияТолько метаданныеOn the Similarities Between Denoising Diffusion Models and Autoencoders(2023) Chervontsev, S.; Trofimov, A.; Трофимов, Александр Геннадьевич