Understanding Fault Tolerance of Adversarially Robust Pruned Models
Manali Dangarikar, Cory Merkel
cs.LG, cs.CR
Submitted: 2026-08-04
Comments: 6 pages, 4 figures, Accepted for oral presentation at the 2026 IEEE National Aerospace and Electronics Conference (NAECON 2026)
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
- Explaining and Harnessing Adversarial Examples
- Towards Deep Learning Models Resistant to Adversarial Attacks
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