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Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence | IEEE Conference Publication | IEEE Xplore

Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence


Abstract:

Increasing the model capacity is a known approach to enhance the adversarial robustness of deep learning networks. On the other hand, various model compression techniques...Show More

Abstract:

Increasing the model capacity is a known approach to enhance the adversarial robustness of deep learning networks. On the other hand, various model compression techniques, including pruning and quantization, can reduce the size of the network while preserving its accuracy. Several recent studies have addressed the relationship between model compression and adversarial robustness, while some experiments have reported contradictory results. This work summarizes available evidence and discusses possible explanations for the observed effects.
Date of Conference: 05-08 December 2023
Date Added to IEEE Xplore: 01 January 2024
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Conference Location: Mexico City, Mexico

References

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