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.3
Paper Title ONLINE WEIGHT PRUNING VIA ADAPTIVE SPARSITY LOSS
Authors George Retsinas, Athena Elafrou, Georgios Goumas, Petros Maragos, National Technical University of Athens, Greece
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 Pruning neural networks has regained interest in recent years as a means to compress state-of-the-art deep neural networks and enable their deployment on resource-constrained devices. In this paper, we propose a robust sparsity controlling framework that efficiently prunes network parameters during training with minimal computational overhead. We incorporate fast mechanisms to prune individual layers and build upon these to automatically prune the entire network under a user-defined budget constraint. Key to our end-to-end network pruning approach is the formulation of an intuitive and easy-to-implement adaptive sparsity loss used to explicitly control sparsity during training, enabling efficient budget-aware optimization.