… tensor ring factorization, we introduce Tensor Ring Networks (TR-Nets), which significantly
compress … Our results show that our TR-Nets approach is able to compress LeNet-5 by 11× …

… large highorder tensor, we only need to store the set of small core tensors as the parameters
of the compressed … -Perpiñán, “Low-rank compression of neural nets: Learning the rank of …

… stack deeper and wider. Tensor decomposition can not only compress DNN models but also
… About tensor ring (TR) decomposition of tensor decomposition, there are two problems: (1) …

… are compressed via tensor ring decomposition. Furthermore, to mitigate the accuracy loss
caused by compression… Thereby, the TT decomposition might result in large intermediate cores …

… low-rank CNNs compression method that is based on reduced storage direct tensor ring …
, and it is characterized by large parameter and FLOPS compression rates, while preserving …

… and large in middle cores, eg, like an olive (Zhao et al. 2016). To address this issue, we
propose to use the tensor ring … tensor and then factorized using TRD. Since this corresponding …

… However, for simplicity the parameter compression in eCNN was not implemented; instead,
we increase the size by 1.5× to 960 KB and 480 KB, respectively, to support large models. …

… , Tensor Train (TT) and Tensor Ring (TR) are common decompositions for low rank compression
of … That means the performance of TR-Nets and TT-Nets in compressing wider neural …

… And our algorithm can compress LeNet5 [12] with compression … and TR-Nets [26] compresses
deep neural network via tensor ring … Further, for large-scale datasets, we will explore more …

… In this paper, we focus on using tensor ring decomposition to compress deep convolutional
neural networks… Note if the input channel I and output channel O are large, we can further de- …