25 citations · 42 across the 3 of their papers we have counts for
3 papers
Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey
Paul Wimmer, Jens Mehnert, Alexandru Paul Condurache
State-of-the-art deep learning models have a parameter count that reaches into the billions. Training, storing and transferring such models is energy and time consuming, thus costl…
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…
FreezeNet: Full Performance by Reduced Storage Costs
Paul Wimmer, Jens Mehnert, Alexandru Condurache
Pruning generates sparse networks by setting parameters to zero. In this work we improve one-shot pruning methods, applied before training, without adding any additional storage co…