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README.md

TernaryNet

pytorch implementation for the paper

Mattias P. Heinrich, Max Blendowski, Ozan Oktay "TernaryNet: Faster Deep Model Inference without GPUs for Medical 3D Segmentation using Sparse and Binary Convolutions" currently under review for IJCARS MICCAI 2017 special issue

see https://arxiv.org/abs/1801.09449

Currently, only the most basic training/validation example using ternary convolutions within a U-Net medical image segmentation pipeline are provided. This will be extended in the near future, also with the addition of Hamming distance optimised C-code for inference.

The proposed ternary hyperbolic tangent activation is defined as

m = torch.nn.Tanh()
y = m((x*beta*2.0-beta))*0.5
y += -m((-x*beta*2.0-beta))*0.5

If you find the material useful please cite the above paper or contact me through my website mpheinrich.de

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