Alexey Ershov (axruff)

axruff

Geek Repo

Company:KIT

Location:Karlsruhe, Germany

Twitter:@axershov

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Alexey Ershov's repositories

cuda-flow3d

3D Optical flow using NVIDIA CUDA

3DUnetCNN

Keras 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation

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spraylab

Spray Lab: tools for spray analysis, visualization and processing

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books

Best books on Computational Sciences

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course-v3

The 3rd edition of course.fast.ai

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deeplearning-models

A collection of various deep learning architectures, models, and tips

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DenseDepth

High Quality Monocular Depth Estimation via Transfer Learning

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DRIT

Learning diverse image-to-image translation from unpaired data

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EfficientNet-PyTorch

A PyTorch implementation of EfficientNet

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FastFCN

FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation.

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flatfield

Adaptive Flat-Field Correction

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GenRe-ShapeHD

Code and data release for GenRe (NeurIPS 2018) and ShapeHD (ECCV 2018)

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matrix

Generic multi-dimensional Matrix in C++ 11/14

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MCAD

OpenSCAD Parametric CAD Library (LGPL 2.1)

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orrb

Code for the paper "OpenAI Remote Rendering Backend"

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PhD-Thesis

My PhD thesis: Automated Analysis of Time-resolved X-ray Data using Optical Flow Methods

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pix2pix

Image-to-image translation with conditional adversarial nets

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PS-FCN

Learning Based Calibrated Photometric Stereo for Non-Lambertian Surface (ECCV 2018)

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rethinking-network-pruning

Rethinking the Value of Network Pruning (Pytorch) (ICLR 2019)

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segmentation_models.pytorch

Segmentation models with pretrained backbones. PyTorch.

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slimmable_networks

Slimmable Networks, ICLR 2019, and ICCV 2019

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Synthetic2Realistic

ECCV 2018 "T2Net: Synthetic-to-Realistic Translation for Depth Estimation Tasks"

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Texture-Synthesis-Using-Convolutional-Neural-Networks

Tensorflow implementation of paper - "Texture Synthesis Using Convolutional Neural Networks"

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UNetPlusPlus

Official Keras Implementation for UNet++ in IEEE Transactions on Medical Imaging and DLMIA 2018

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vid2vid

Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.

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vision

Datasets, Transforms and Models specific to Computer Vision

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