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Generative Models for Fashion Industry using Deep Neural Networks
P. 1–6.
Ildar Lomov, Makarov I.
The progress of deep learning models in image and video processing leads to new artificial intelligence applications in Fashion industry. We consider the application of Generative Adversarial Networks and Neural Style Transfer for Digital Fashion presented as Virtual fashion for trying new clothes. Our model generate humans in clothes with respect to different fashion preferences, color layouts and fashion style. We propose that the virtual fashion industry will be highly impacted by accuracy of generating personalized human model taking into account different aspects of product and human preferences. We compare our model with state-of-art VITON model and show that using new perceptual loss in deep neural network architecture lead to better qualitative results in generating humans in clothes.
Razzhigaev A., Voronov A., Kaznacheev A. et al., International Conference on Computational Linguistics, 2022.
Pixel-level autoregression with Transformer models (Image GPT or iGPT) is one of the recent approaches to image generation that has not received massive attention and elaboration due to quadratic complexity of attention as it imposes huge memory requirements and thus restricts the resolution of the generated images. In this paper, we propose to tackle this ...
Added: June 7, 2023
Aleksandrova T. I., Lola G., Design Journal 2017 Vol. 20 P. 4584–4591
The article discusses the issues related to the construction the future and the responsibility it entails. As a social agent, the designer usually has a pragmatic and instrumental relation with the future. However, any attempts to construct the future in a rigid manner will likely lead to the reproduction of the existing algorithms of thinking ...
Added: February 2, 2022
Ratnikov F., Rogachev A., , in: EPJ Web of ConferencesVol. 251: 25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021).: EDP Sciences, 2021. Ch. 03043.
Added: September 14, 2021
A. Maevskiy, F. Ratnikov, Zinchenko A. et al., The European Physical Journal C - Particles and Fields 2021 Vol. 81 Article 599
High energy physics experiments rely heavily on the detailed detector simulation models in many tasks. Running these detailed models typically requires a notable amount of the computing time available to the experiments. In this work, we demonstrate a new approach to speed up the simulation of the Time Projection Chamber tracker of the MPD experiment at ...
Added: July 12, 2021
Maksim Golyadkin, Makarov I., , in: Analysis of Images, Social Networks and Texts: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected PapersVol. 12602.: Springer, 2021. P. 230–242.
Manga colorization is time-consuming and hard to automate. In this paper, we propose a conditional adversarial deep learning approach for semi-automatic manga images colorization. The system directly maps a tuple of grayscale manga page image and sparse color hint constructed by the user to an output colorization. High-quality colorization can be obtained in a fully ...
Added: April 9, 2021
Lomov I., Lyubimov M., Makarov I. et al., Journal of Industrial Information Integration 2021 Vol. 23 Article 100216
Automated early process fault detection and prediction remains a challenging problem in industrial processes. Traditionally it has been done by multivariate statistical analysis of sensor readings and, more recently, with the help of machine learning methods. The quality of machine learning models strongly depends on feature engineering, that in turn heavily relies on expertise of ...
Added: March 21, 2021
Voynov A., Babenko A., , in: International Conference on Machine Learning (ICML 2020)Vol. 119.: PMLR, 2020. P. 9728–9738.
Added: January 14, 2021
Alanov A., Kochurov M., Volkhonskiy D. et al., , in: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2020)Vol. 4.: SciTePress, 2020. P. 214–221.
We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism. The user control ability allows to explicitly specify the texture which should be generated by the model. This property follows from using an encoder part which learns a latent representation for each texture from the dataset. To ensure ...
Added: November 8, 2020
A Maevskiy, D Derkach, N Kazeev et al., Journal of Physics: Conference Series 2020 Vol. 1525 No. 012097 P. 1–6
The increasing luminosities of future Large Hadron Collider runs and next generation of collider experiments will require an unprecedented amount of simulated events to be produced. Such large scale productions are extremely demanding in terms of computing resources. Thus new approaches to event generation and simulation of detector responses are needed. In LHCb, the accurate ...
Added: July 27, 2020
Struminsky K., Vetrov D., Lecture Notes in Computer Science 2019 Vol. 11832 P. 81–93
Theoretical analysis in [1] suggested that adversarially trained generative models are naturally inclined to learn distribution with low support. In particular, this effect is caused by the limited capacity of the discriminator network. To verify this claim, [2] proposed a statistical test based on the birthday paradox that partially confirmed the analysis. In this paper, ...
Added: April 23, 2020
Soboleva Natalia, Yakovlev K., , in: Proceedings of the 42nd German Conference on Artificial Intelligence (KI 2019), Kassel, Germany, September 23-26, 2019.: Springer, 2019. P. 316–324.
2D path planning in static environment is a well-known problem and one of the common ways to solve it is to (1) represent the environment as a grid and (2) perform a heuristic search for a path on it. At the same time 2D grid resembles much a digital image, thus an appealing idea comes ...
Added: February 3, 2020
Kazeev N., Derkach D., Ratnikov F. et al., Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2019
Added: July 11, 2019
Derkach D., Kazeev N., Ratnikov F. et al., Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2020 Vol. 952 No. 0168-9002 P. 161804
We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details. This network is trained to reproduce high level features of the simulated detector events based on input observables of incident particles. This allows the dramatic increase of simulation speed. We demonstrate that this approach provides simulation ...
Added: February 11, 2019
Osokin A., Chessel A., Carazo Salas R. et al., , in: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017).: Venice: IEEE, 2017. P. 2252–2261.
In this paper, we propose a novel application of Generative Adversarial Networks (GAN) to the synthesis of cells imaged by fluorescence microscopy. Compared to natural images, cells tend to have a simpler and more geometric global structure that facilitates image generation. However, the correlation between the spatial pattern of different fluorescent proteins reflects important biological ...
Added: October 19, 2017