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Deep Learning Based Computed Tomography Whys and Wherefores
This is an article about the Computed Tomography (CT) and how Deep Learn...
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Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
This work presents Kornia -- an open source computer vision library whic...
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A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
This work presents Kornia, an open source computer vision library built ...
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Differentiable Computational Geometry for 2D and 3D machine learning
With the growth of machine learning algorithms with geometry primitives,...
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Accelerating 3D Deep Learning with PyTorch3D
Deep learning has significantly improved 2D image recognition. Extending...
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MIOpen: An Open Source Library For Deep Learning Primitives
Deep Learning has established itself to be a common occurrence in the bu...
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PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch
We present a new open source python package, based on PyLightcurve and P...
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TorchRadon: Fast Differentiable Routines for Computed Tomography
This work presents TorchRadon – an open source CUDA library which contains a set of differentiable routines for solving computed tomography (CT) reconstruction problems. The library is designed to help researchers working on CT problems to combine deep learning and model-based approaches. The package is developed as a PyTorch extension and can be seamlessly integrated into existing deep learning training code. Compared to the existing Astra Toolbox, TorchRadon is up to 125 faster. The operators implemented by TorchRadon allow the computation of gradients using PyTorch backward(), and can therefore be easily inserted inside existing neural networks architectures. Because of its speed and GPU support, TorchRadon can also be effectively used as a fast backend for the implementation of iterative algorithms. This paper presents the main functionalities of the library, compares results with existing libraries and provides examples of usage.
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