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Arxiv
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Holistic Adversarially Robust Pruning
- Neural networks can be drastically shrunk in size by removing redundant parameters.
- Compression often leads to a drop in accuracy and lack of adversarial robustness.
- A new method called HARP copes with aggressive pruning better than previous approaches.
- HARP optimizes the compression rate and scoring connections for each layer individually, maintaining accuracy and robustness with a 99% reduction in network size.
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