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cs.CV2023
Data-Free Dynamic Compression of CNNs for Tractable Efficiency
Lukas Meiner, Jens Mehnert, Alexandru Paul Condurache
To reduce the computational cost of convolutional neural networks (CNNs) on resource-constrained devices, structured pruning approaches have shown promise in lowering floating-poin…
cs.CV2022★ 1 cited
Interspace Pruning: Using Adaptive Filter Representations to Improve Training of Sparse CNNs
Paul Wimmer, Jens Mehnert, Alexandru Paul Condurache
Unstructured pruning is well suited to reduce the memory footprint of convolutional neural networks (CNNs), both at training and inference time. CNNs contain parameters arranged in…