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EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
Chaoqi Wang · Roger Grosse · Sanja Fidler · Guodong Zhang

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #22

Reducing the test time resource requirements of a neural network while preserving test accuracy is crucial for running inference on resource-constrained devices. To achieve this goal, we introduce a novel network reparameterization based on the Kronecker-factored eigenbasis (KFE), and then apply Hessian-based structured pruning methods in this basis. As opposed to existing Hessian-based pruning algorithms which do pruning in parameter coordinates, our method works in the KFE where different weights are approximately independent, enabling accurate pruning and fast computation. We demonstrate empirically the effectiveness of the proposed method through extensive experiments. In particular, we highlight that the improvements are especially significant for more challenging datasets and networks. With negligible loss of accuracy, an iterative-pruning version gives a 10x reduction in model size and a 8x reduction in FLOPs on wide ResNet32.

Author Information

Chaoqi Wang (University of Toronto)
Roger Grosse (University of Toronto and Vector Institute)
Sanja Fidler (University of Toronto, NVIDIA)
Guodong Zhang (University of Toronto)

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