Holistic Adversarially Robust Pruning
Qi Zhao, Christian Wressnegger

TL;DR
HARP is a novel holistic pruning method that significantly compresses neural networks by 99% while preserving accuracy and adversarial robustness, using a dynamic, layer-specific compression strategy.
Contribution
HARP introduces a global, adaptive pruning approach that balances compression, accuracy, and robustness through incremental regularization and layer-wise optimization.
Findings
Maintains accuracy and robustness at 99% compression.
Layer-specific non-uniform compression improves results.
Dynamic regularization effectively balances multiple objectives.
Abstract
Neural networks can be drastically shrunk in size by removing redundant parameters. While crucial for the deployment on resource-constraint hardware, oftentimes, compression comes with a severe drop in accuracy and lack of adversarial robustness. Despite recent advances, counteracting both aspects has only succeeded for moderate compression rates so far. We propose a novel method, HARP, that copes with aggressive pruning significantly better than prior work. For this, we consider the network holistically. We learn a global compression strategy that optimizes how many parameters (compression rate) and which parameters (scoring connections) to prune specific to each layer individually. Our method fine-tunes an existing model with dynamic regularization, that follows a step-wise incremental function balancing the different objectives. It starts by favoring robustness before shifting focus…
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Taxonomy
TopicsPhysical Unclonable Functions (PUFs) and Hardware Security · Advanced Malware Detection Techniques
MethodsPruning · Focus
