In book
[б.и.], 2021.
Yadav N. K., Singh S. K., Dubey S. R., Neural Computing and Applications 2023 Vol. 35 No. 27 P. 19729–19749
In the recent improvement in deep learning approaches for realistic image generation and translation, Generative Adversarial Networks (GANs) delivered favorable results. GAN generates novel samples that look indistinguishable from authentic images. This paper proposes a novel generative network for thermal-to-visible image translation. Thermal to Visible synthesis is challenging due to the non-availability of accurate semantic ...
Added: October 23, 2023
Nand Kumar Yadav, Singh S. K., Dubey S. R., Applied Intelligence 2022 No. 52 P. 12704–12723
Generative Adversarial Network (GAN) is one of the recent developments in the area of deep learning to transform the images from one domain to another domain. While transforming the images, we need to make sure that the background information should not influence the learning process. The attention-based networks are developed to learn the saliency maps ...
Added: October 23, 2023
Yadav N. K., Singh S. K., Dubey S. R., IEEE Transactions on Instrumentation and Measurement 2022 Vol. 71 P. 1–9
Deep learning has recently shown outstanding performance for different applications, including image-to-image translation by generative adversarial networks (GANs). However, GAN models are very complex as build with multiple deep networks and require huge computational resources for the training and inference. Hence, the real-time deployment of GAN models is not feasible at present. In this article, ...
Added: October 23, 2023
Zunin V., Romanov A., Проблемы разработки перспективных микро- и наноэлектронных систем (МЭС) 2021 № 2 С. 83–90
The paper provides an overview of the current state of the implementation of neural networks and their methods of execution. A detailed description of the main components of the Intel® OpenVINO ™ Toolkit for executing neural networks on various Intel hardware platforms (CPU, GPU, Neural Compute Stick 2) is considered. The works in which this ...
Added: February 15, 2023
Ratnikov F., Rogachev A., Mokhnenko S. et al., Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2023 Vol. 1046 Article 167591
The abundance of data arriving in the new runs of the Large Hadron Collider creates tough requirements for the amount of necessary simulated events and thus for the speed of generating such events. Current approaches can suffer from long generation time and lack of important storage resources to preserve the simulated datasets. The development of ...
Added: October 29, 2022
Khrulkov V., Babenko A., Oseledets I., , in: Proceedings of the 38th International Conference on Machine Learning (ICML 2021)Vol. 139.: PMLR, 2021. P. 5432–5442.
Recent work demonstrated the benefits of studying continuous-time dynamics governing the GAN training. However, this dynamics is analyzed in the model parameter space, which results in finite-dimensional dynamical systems. We propose a novel perspective where we study the local dynamics of adversarial training in the general functional space and show how it can be represented ...
Added: December 27, 2021
Cherepkov A., Voynov A., Babenko A., , in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.: IEEE, 2021. P. 3671–3680.
Added: December 27, 2021
Morozov S., Voynov A., Babenko A., , in: Proceedings of the 9th International Conference on Learning Representations (ICLR 2021). ICLR, 2021.: ICLR, 2021. P. 1–17.
Added: December 27, 2021
Ratnikov F., Ustyuzhanin A., EPJ Web of Conferences 2019 Vol. 2014 P. 1–8
Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider (HL-LHC) needs, so the experiments are in urgent need of new fast simulation techniques. We ...
Added: October 9, 2019
Ratnikov F., Zakharov E., Proceedings of Science Italy 2019 Vol. 340 P. 1–3
In HEP experiments CPU resources required by MC simulations are constantly growing and become a very large fraction of the total computing power (greater than 75\%). At the same time the pace of performance improvements from technology is slowing down, so the only solution is a more efficient use of resources. Efforts are ongoing in ...
Added: October 9, 2019