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Provable Filter Pruning for Efficient Neural Networks
[article]
2020
arXiv
pre-print
We present a provable, sampling-based approach for generating compact Convolutional Neural Networks (CNNs) by identifying and removing redundant filters from an over-parameterized network. Our algorithm uses a small batch of input data points to assign a saliency score to each filter and constructs an importance sampling distribution where filters that highly affect the output are sampled with correspondingly high probability. In contrast to existing filter pruning approaches, our method is
arXiv:1911.07412v2
fatcat:l5drcoblgvdxfcksho5g7inhue