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Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition

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Figure

Figure 1: Tensor diagrams. (a) shows the graphical repre-sentation of the matrix M ∈ RL1×L2, where L1 and L2denote the matrix size
Figure 3: Two representations of tensor ring decomposition(TRD). In Figure 3(a), TRD is expounded in the traditionalwhereRway: the core tensors are multiplied one by one, and forma ring structure
Figure 7: The results of “End-to-End Training” on UCF11dataset. (a) shows the different compression ratio based onthe vanilla LSTM
Table 3: Comparison with state-of-the-art results onHMDB51. The best accuracy is 0.664 from the I3D modelreported in (Carreira and Zisserman 2017), which bases on3D ConvNets and is not RNN-based method.

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