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Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack.

, , and . CoRR, (2018)

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BD-NET: A Multiplication-Less DNN with Binarized Depthwise Separable Convolution., , , and . ISVLSI, page 130-135. IEEE Computer Society, (2018)Robust Machine Learning via Privacy/ Rate-Distortion Theory., , , , and . ISIT, page 1320-1325. IEEE, (2021)Towards Universal Adversarial Examples and Defenses., , , , , and . ITW, page 1-6. IEEE, (2021)Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack., , and . CVPR, page 588-597. Computer Vision Foundation / IEEE, (2019)Leveraging Noise and Aggressive Quantization of In-Memory Computing for Robust DNN Hardware Against Adversarial Input and Weight Attacks., , , , , and . DAC, page 559-564. IEEE, (2021)Threshold Breaker: Can Counter-Based RowHammer Prevention Mechanisms Truly Safeguard DRAM?, , , , , and . CoRR, (2023)Bit-Flip Attack: Crushing Neural Network With Progressive Bit Search., , and . ICCV, page 1211-1220. IEEE, (2019)Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples., , , and . CoRR, (2018)DA2: Deep Attention Adapter for Memory-EfficientOn-Device Multi-Domain Learning., , and . CoRR, (2020)EMGAN: Early-Mix-GAN on Extracting Server-Side Model in Split Federated Learning., , , , , and . AAAI, page 13545-13553. AAAI Press, (2024)