PRF: deep neural network compression by systematic pruning of redundant filters

Author:

Sarvani C. H.ORCID,Ghorai Mrinmoy,Basha S. H. Shabbeer

Funder

Nvidia

Publisher

Springer Science and Business Media LLC

Reference44 articles.

1. Arthur D, Vassilvitskii S (2006) k-means++: the advantages of careful seeding. In: Technical report, Stanford

2. Ayinde BO, Inanc T, Zurada JM (2019) Redundant feature pruning for accelerated inference in deep neural networks. Neural Netw 118:148–158

3. Basha SHS, Farazuddin M, Pulabaigari V, Dubey SR, Mukherjee S (2024) Deep model compression based on the training history. Neurocomputing 573:127257

4. Chung FRK (1997) Spectral graph theory, vol 92. American Mathematical Soc

5. Chung KL, Chang YL (2023) An Effective Backward Filter Pruning Algorithm Using K1, n Bipartite Graph-Based Clustering and the Decreasing Pruning Rate Approach. Authorea Preprints

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