Papers › Neural Network Compression Using Higher-Order Statistics and AuxiliaryReconstruction Losses
Neural Network Compression Using Higher-Order Statistics and AuxiliaryReconstruction Losses
Christos Chatzikonstantinou, Georgios Th. Papadopoulos, Kosmas Dimitropoulos, Petros Daras
In this paper, the problem of pruning and compressingthe weights of various layers of deep neural networks is in-vestigated. The proposed method aims to remove redundantfilters from the network to reduce computational complex-ity and storage requirements, while improving the perfor-mance of the original network. More specifically, a novelfilter selection criterion is introduced based on the fact thatfilters whose weights follow a Gaussian distribution corre-spond to hidden units that do not capture important aspectsof data. To this end, Higher Order Statistics (HOS) areused and filters with low cumulant values that do not de-viate significantly from Gaussian distribution are identifiedand removed from the network. In addition, a novel prun-ing strategy is proposed aiming to decide on the pruningratio of each layer using the Shapiro-Wilk normality test.The use of auxiliary MSE losses (intermediate and afterthe softmax layer) during the fine-tuning phase further im-proves the overall performance of the compressed network.Extensive experiments with different network architecturesand comparison with state-of-the-art approaches on well-known public datasets, such as CIFAR-10, CIFAR-100 andILSCVR-12, demonstrate the great potential of the proposedapproach.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections