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Found 7 papers, 2 papers with code
RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks
Overall, CapsNets achieve better robustness against adversarial examples and affine transformations, compared to a traditional CNN with a similar number of parameters.
ISimDL: Importance Sampling-Driven Acceleration of Fault Injection Simulations for Evaluating the Robustness of Deep Learning
Using importance sampling in FAT reduces the overhead required for finding faults that lead to a predetermined drop in accuracy by more than 12x.
enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks
Towards better reliability analysis for DNNs, we present enpheeph, a Fault Injection Framework for Spiking and Compressed DNNs.
NeuroUnlock: Unlocking the Architecture of Obfuscated Deep Neural Networks
We release the NeuroUnlock and the ReDLock as open-source frameworks.
MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences
Training of the policy is supported by Machine Learning-based analytical models for quick performance estimation, thereby drastically reducing the time spent for dynamic profiling.
Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks
Capsule Networks (CapsNets), recently proposed by the Google Brain team, have superior learning capabilities in machine learning tasks, like image classification, compared to the traditional CNNs.
FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks
The goal is to reduce the hardware requirements of CapsNets by removing unused/redundant connections and capsules, while keeping high accuracy through tests of different learning rate policies and batch sizes.
