2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-NNC.8
Paper Title HYBRID PRUNING AND SPARSIFICATION
Authors Hamed Rezazadegan Tavakoli, Nokia Technologies, Finland; Joachim Wabnig, Nokia Bell Labs, Finland; Francesco Cricri, Honglei Zhang, Emre Aksu, Nokia Technologies, Finland; Iraj Saniee, Nokia Bell Labs, Finland
SessionSS-NNC: Special Session: Neural Network Compression and Compact Deep Features
LocationArea B
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Special Sessions: Neural Network Compression and Compact Deep Features: From Methods to Standards
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Abstract A hybrid approach based on the combination of saliency-based neural pruning and regularization-based sparsification is proposed. We propose using a graph diffusion process for determining the neuron importance for pruning. Then, we use a regularization loss based on weighted L1-norm and L2-norm during fine-tuning to recover the lost performance. This is followed by a threshold step to further impose sparsification. We demonstrate such a hybrid approach achieves significantly better performance in comparison to purely regularization-based sparsification for large neural networks. To this end, we assessed our proposed method on three tasks, including: image classification (3 network architectures), audio classification and image compression.