HexagDLy - Processing hexagonally sampled data with CNNs in PyTorch
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Abstract (arXiv)
HexagDLy is a Python-library extending the PyTorch deep learning framework with convolution and pooling operations on hexagonal grids. It aims to ease the access to convolutional neural networks for applications that rely on hexagonally sampled data as, for example, commonly found in ground-based astroparticle physics experiments.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Mar 2019.
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Citing papers per year: 2021: 5; 2022: 2; 2023: 0; 2024: 1; 2025: 1.
9 papers.
- 2021Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizescited by 8
- 2021HexCNN: A Framework for Native Hexagonal Convolutional Neural Networkscited by 2
- 2022Exploring the impact of spatiotemporal granularity on the demand prediction of dynamic ride-hailingcited by 1
- 2021Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Generationcited by 0
- 2021Resampling and super-resolution of hexagonally sampled images using deep learningcited by 0
- 2021A deep ensemble approach to X-ray polarimetrycited by 0
- 2022Biologically inspired deep residual networks for computer vision applicationscited by 0
- 2024Localized Observation Abstraction Using Piecewise Linear Spatial Decay for Reinforcement Learning in Combat Simulationscited by 0
- 2025Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networkscited by 0
- 2025Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networks19 Feb 2025
- 2024Localized Observation Abstraction Using Piecewise Linear Spatial Decay for Reinforcement Learning in Combat Simulations23 Aug 2024
- 2022Biologically inspired deep residual networks for computer vision applications5 May 2022
- 2022Exploring the impact of spatiotemporal granularity on the demand prediction of dynamic ride-hailing19 Mar 2022
- 2021A deep ensemble approach to X-ray polarimetry4 Nov 2021
- 2021Resampling and super-resolution of hexagonally sampled images using deep learning3 Nov 2021
- 2021Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes18 Oct 2021
- 2021HexCNN: A Framework for Native Hexagonal Convolutional Neural Networks25 Jan 2021
- 2021Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Generation1 Jan 2021