PR-DARTS: Pruning-Based Differentiable Architecture Search. (arXiv:2207.06968v1 [cs.CV])

The deployment of Convolutional Neural Networks (CNNs) on edge devices is
hindered by the substantial gap between performance requirements and available
processing power. While recent research has made large strides in developing
network pruning methods for reducing the computing overhead of CNNs, there
remains considerable accuracy loss, especially at high pruning ratios.
Questioning that the architectures designed for non-pruned networks might not
be effective for pruned networks, we propose to search architectures for
pruning methods by defining a new search …

architecture arxiv cv pr pruning search