Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer

CVPR 2020 Yerlan Idelbayev Miguel A. Carreira-Perpinan

Neural net compression can be achieved by approximating each layer's weight matrix by a low-rank matrix. The real difficulty in doing this is not in training the resulting neural net (made up of one low-rank matrix per layer), but in determining what the optimal rank of each layer is--effectively, an architecture search problem with one hyperparameter per layer... (read more)

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