Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring

7 January 2020

Papers citing "Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring"

9 / 9 papers shown

Title
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 79 900 0 18 Feb 2019
Lipschitz regularity of deep neural networks: analysis and efficient estimation Kevin Scaman Aladin Virmaux 75 527 0 28 May 2018
Sigsoftmax: Reanalysis of the Softmax Bottleneck Sekitoshi Kanai Yasuhiro Fujiwara Yuki Yamanaka S. Adachi 43 69 0 28 May 2018
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks Yusuke Tsuzuku Issei Sato Masashi Sugiyama AAML 103 306 0 12 Feb 2018
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach Tsui-Wei Weng Huan Zhang Pin-Yu Chen Jinfeng Yi D. Su Yupeng Gao Cho-Jui Hsieh Luca Daniel AAML 76 467 0 31 Jan 2018
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric Richard Y. Zhang Phillip Isola Alexei A. Efros Eli Shechtman Oliver Wang EGVM 327 11,734 0 11 Jan 2018
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 117 2,145 0 21 Aug 2017
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era Chen Sun Abhinav Shrivastava Saurabh Singh Abhinav Gupta VLM 149 2,393 0 10 Jul 2017
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 229 14,893 1 21 Dec 2013