LAASP prunes neural networks during training by greedily selecting the best layer and filter-importance criterion at each step using the network's loss on a data subset.
Filter pruning by switching to neighboring cnns with good attributes,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration
LAASP prunes neural networks during training by greedily selecting the best layer and filter-importance criterion at each step using the network's loss on a data subset.