Výsledky vyhledávání - "Volkhonskiy, Denis"

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Autor: Rakhimov, Ruslan, Volkhonskiy, Denis, Artemov, Alexey, Zorin, Denis, Burnaev, Evgeny

The video generation task can be formulated as a prediction of future video frames given some past frames. Recent generative models for videos face the problem of high computational requirements. Some models require up to 512 Tensor Processing Units

Externí odkaz: http://arxiv.org/abs/2006.10704

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Deep Vectorization of Technical Drawings

Autor: Egiazarian, Vage, Voynov, Oleg, Artemov, Alexey, Volkhonskiy, Denis, Safin, Aleksandr, Taktasheva, Maria, Zorin, Denis, Burnaev, Evgeny

We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images. Our method includes (1) a deep learning-based cleaning stage to eliminate the background and imperfections in the im

Externí odkaz: http://arxiv.org/abs/2003.05471

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User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks

Autor: Alanov, Aibek, Kochurov, Max, Volkhonskiy, Denis, Yashkov, Daniil, Burnaev, Evgeny, Vetrov, Dmitry

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 proper

Externí odkaz: http://arxiv.org/abs/1904.04751

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Reconstruction of 3D Porous Media From 2D Slices

Autor: Volkhonskiy, Denis, Muravleva, Ekaterina, Sudakov, Oleg, Orlov, Denis, Belozerov, Boris, Burnaev, Evgeny, Koroteev, Dmitry

In many branches of earth sciences, the problem of rock study on the micro-level arises. However, a significant number of representative samples is not always feasible. Thus the problem of the generation of samples with similar properties becomes act

Externí odkaz: http://arxiv.org/abs/1901.10233

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Inductive Conformal Martingales for Change-Point Detection

Autor: Volkhonskiy, Denis, Nouretdinov, Ilia, Gammerman, Alexander, Vovk, Vladimir, Burnaev, Evgeny

We consider the problem of quickest change-point detection in data streams. Classical change-point detection procedures, such as CUSUM, Shiryaev-Roberts and Posterior Probability statistics, are optimal only if the change-point model is known, which

Externí odkaz: http://arxiv.org/abs/1706.03415

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Steganographic Generative Adversarial Networks

Autor: Volkhonskiy, Denis, Nazarov, Ivan, Burnaev, Evgeny

Steganography is collection of methods to hide secret information ("payload") within non-secret information "container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possi

Externí odkaz: http://arxiv.org/abs/1703.05502

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