no code implementations • 13 Apr 2017 • Vladimir Golkov, Marcin J. Skwark, Atanas Mirchev, Georgi Dikov, Alexander R. Geanes, Jeffrey Mendenhall, Jens Meiler, Daniel Cremers
In this paper, we show that deep learning can predict biological function of molecules directly from their raw 3D approximated electron density and electrostatic potential fields.
1 code implementation • 14 Jan 2019 • Georgi Dikov, Patrick van der Smagt, Justin Bayer
In this paper we propose a Bayesian method for estimating architectural parameters of neural networks, namely layer size and network depth.
3 code implementations • ICCV 2021 • Elias Kassapis, Georgi Dikov, Deepak K. Gupta, Cedric Nugteren
To this end, we propose a novel two-stage, cascaded approach for calibrated adversarial refinement: (i) a standard segmentation network is trained with categorical cross entropy to predict a pixelwise probability distribution over semantic classes and (ii) an adversarially trained stochastic network is used to model the inter-pixel correlations to refine the output of the first network into coherent samples.
no code implementations • ICCV 2023 • Jihong Ju, Ching Wei Tseng, Oleksandr Bailo, Georgi Dikov, Mohsen Ghafoorian
A key challenge in neural 3D scene reconstruction from monocular images is to fuse features back projected from various views without any depth or occlusion information.
no code implementations • ICCV 2023 • Xuepeng Shi, Georgi Dikov, Gerhard Reitmayr, Tae-Kyun Kim, Mohsen Ghafoorian
Self-supervised monocular depth estimation (SSMDE) aims at predicting the dense depth maps of monocular images, by learning to minimize a photometric loss using spatially neighboring image pairs during training.
no code implementations • 22 Mar 2024 • Florian Langer, Jihong Ju, Georgi Dikov, Gerhard Reitmayr, Mohsen Ghafoorian
In contrast to previous works, we directly predict alignment parameters and shape embeddings.