On CIFAR-10, quantization-aware training with large constant scaling factors improves noise robustness, but noisy training (injecting matching Gaussian noise during training) gives far larger robustness gains, and quantization adds little on top of it.
Exploring the impact of random tele- graph noise-induced accuracy loss on resistive RAM- based deep neural network
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
other 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
other 1polarities
unclear 1representative citing papers
citing papers explorer
-
On Hardening DNNs against Noisy Computations
On CIFAR-10, quantization-aware training with large constant scaling factors improves noise robustness, but noisy training (injecting matching Gaussian noise during training) gives far larger robustness gains, and quantization adds little on top of it.