  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.
