AutoDFP: Automatic Data-Free Pruning via Channel Similarity Reconstruction

Structured pruning methods are developed to bridge the gap between the massive scale of neural networks and the limited hardware resources. Most current structured pruning methods rely on training datasets to fine-tune the compressed model, resulting in high computational burdens and being inapplica...

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Veröffentlicht in:	arXiv.org 2024-03
Hauptverfasser:	Li, Siqi, Chen, Jun, Jingyang Xiang, Zhu, Chengrui, Liu, Yong
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Channels Datasets Neural networks Pruning Reconstruction Similarity
Online-Zugang:	Volltext
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