This library brings [Spatially-sparse convolutional networks](https://github.com/btgraham/SparseConvNet) to Torch/PyTorch. Moreover, it introduces **Valid Sparse Convolutions**, that can be used to build computationally efficient sparse VGG/ResNet/DenseNet-style networks.
This library brings [Spatially-sparse convolutional networks](https://github.com/btgraham/SparseConvNet) to Torch/PyTorch. Moreover, it introduces **Valid Sparse Convolutions**, that can be used to build computationally efficient sparse VGG/ResNet/DenseNet-style networks.
With regular 3x3 convolutions, the set of active (non-zero) sites grows rapidly:<br/>
With regular 3x3 convolutions, the set of active (non-zero) sites grows rapidly:<br/>
With **Valid Sparse Convolutions**, the set of active sites is unchanged. Active sites look at their active neighbors (green); non-active sites (red) have no computational overhead: <br/>
With **Valid Sparse Convolutions**, the set of active sites is unchanged. Active sites look at their active neighbors (green); non-active sites (red) have no computational overhead: <br/>
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Stacking Sparse Valid Convolutions to build VGG and ResNet type ConvNets, information can flow along lines or surfaces of active points.<br/>
Stacking Sparse Valid Convolutions to build VGG and ResNet type ConvNets, information can flow along lines or surfaces of active points.<br/>
