Sparse Mixture Once-for-all Adversarial Training for Efficient In-Situ Trade-Off Between Accuracy and Robustness of DNNs

27 December 2022

Papers citing "Sparse Mixture Once-for-all Adversarial Training for Efficient In-Situ Trade-Off Between Accuracy and Robustness of DNNs"

7 / 7 papers shown

Title
BMPQ: Bit-Gradient Sensitivity Driven Mixed-Precision Quantization of DNNs from Scratch Souvik Kundu Shikai Wang Qirui Sun Peter A. Beerel Massoud Pedram MQ 54 18 0 24 Dec 2021
ShiftAddNet: A Hardware-Inspired Deep Network Haoran You Xiaohan Chen Yongan Zhang Chaojian Li Sicheng Li Zihao Liu Zhangyang Wang Yingyan Lin OOD MQ 101 77 0 24 Oct 2020
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 93 1,776 0 30 May 2018
SkipNet: Learning Dynamic Routing in Convolutional Networks Xin Wang Feng Yu Zi-Yi Dou Trevor Darrell Joseph E. Gonzalez 75 633 0 26 Nov 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 269 12,029 0 19 Jun 2017
Wide Residual Networks Sergey Zagoruyko N. Komodakis 312 7,971 0 23 May 2016
End to End Learning for Self-Driving Cars Mariusz Bojarski D. Testa Daniel Dworakowski Bernhard Firner B. Flepp ... Urs Muller Jiakai Zhang Xin Zhang Jake Zhao Karol Zieba SSL 77 4,163 0 25 Apr 2016