no code implementations • 25 Mar 2024 • Nikita Durasov, Doruk Oner, Jonathan Donier, Hieu Le, Pascal Fua
Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance.
no code implementations • 21 Nov 2022 • Nikita Durasov, Nik Dorndorf, Pascal Fua
Active Learning (AL) can be used to reduce this burden.
no code implementations • 21 Nov 2022 • Nikita Durasov, Nik Dorndorf, Hieu Le, Pascal Fua
Sampling-free approaches can be faster but suffer from other drawbacks, such as lower reliability of uncertainty estimates, difficulty of use, and limited applicability to different types of tasks and data.
1 code implementation • ICCV 2023 • Artem Sevastopolsky, Yury Malkov, Nikita Durasov, Luisa Verdoliva, Matthias Nießner
We show that a simple approach based on fine-tuning pSp encoder for StyleGAN allows us to improve upon the state-of-the-art facial recognition and performs better compared to training on synthetic face identities.
no code implementations • 28 Sep 2021 • Nikita Durasov, Artem Lukoyanov, Jonathan Donier, Pascal Fua
Graph Neural Networks (GNNs) can predict the performance of an industrial design quickly and accurately and be used to optimize its shape effectively.
1 code implementation • CVPR 2022 • Weizhe Liu, Nikita Durasov, Pascal Fua
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density.
3 code implementations • CVPR 2021 • Nikita Durasov, Timur Bagautdinov, Pierre Baque, Pascal Fua
Our central intuition is that there is a continuous spectrum of ensemble-like models of which MC-Dropout and Deep Ensembles are extreme examples.
no code implementations • 20 Nov 2018 • Nikita Durasov, Mikhail Romanov, Valeriya Bubnova, Pavel Bogomolov, Anton Konushin
Monocular depth estimation is the task of obtaining a measure of distance for each pixel using a single image.
Indoor Monocular Depth Estimation Monocular Depth Estimation