Tensor Decomposition Networks (TDNs) prevail for their inherent compact architectures. To give more researchers a flexible way to exploit TDNs, we present a Pytorch toolkit named TedNet. TedNet implements 5 kinds of tensor decomposition(i.e., CANDECOMP/PARAFAC (CP), Block-Term Tucker (BTT), Tucker-2, Tensor Train (TT) and Tensor Ring (TR) on traditional deep neural layers, the convolutional layer and the fully-connected layer. By utilizing the basic layers, it is simple to construct a variety of TDNs. TedNet is available at https://github.com/tnbar/tednet.
@article{arxiv.2104.05018,
title = {TedNet: A Pytorch Toolkit for Tensor Decomposition Networks},
author = {Yu Pan and Maolin Wang and Zenglin Xu},
journal= {arXiv preprint arXiv:2104.05018},
year = {2021}
}