LTD: Low Temperature Distillation for Robust Adversarial Training

3 November 2021

Papers citing "LTD: Low Temperature Distillation for Robust Adversarial Training"

23 / 23 papers shown

Title
Revisiting the Relationship between Adversarial and Clean Training: Why Clean Training Can Make Adversarial Training Better MingWei Zhou Xiaobing Pei AAML 141 0 0 30 Mar 2025
LISArD: Learning Image Similarity to Defend Against Gray-box Adversarial Attacks Joana Cabral Costa Tiago Roxo Hugo Manuel Proença Pedro R. M. Inácio AAML 55 0 0 27 Feb 2025
Democratic Training Against Universal Adversarial Perturbations Bing-Jie Sun Jun Sun Wei Zhao AAML 57 0 0 08 Feb 2025
Dynamic Guidance Adversarial Distillation with Enhanced Teacher Knowledge Hyejin Park Dongbo Min AAML 34 2 0 03 Sep 2024
Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness Erh-Chung Chen Pin-Yu Chen I-Hsin Chung Che-Rung Lee 32 2 0 28 Jun 2024
On adversarial training and the 1 Nearest Neighbor classifier Amir Hagai Yair Weiss AAML 52 0 0 09 Apr 2024
Machine Learning Robustness: A Primer Houssem Ben Braiek Foutse Khomh AAML OOD 34 5 0 01 Apr 2024
Indirect Gradient Matching for Adversarial Robust Distillation Hongsin Lee Seungju Cho Changick Kim AAML FedML 48 2 0 06 Dec 2023
Topology-Preserving Adversarial Training Xiaoyue Mi Fan Tang Yepeng Weng Danding Wang Juan Cao Sheng Tang Peng Li Yang Liu 51 1 0 29 Nov 2023
IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks Yue Cao Tianlin Li Xiaofeng Cao Ivor Tsang Yang Liu Qing-Wu Guo AAML 23 2 0 18 Oct 2023
Revisiting and Advancing Adversarial Training Through A Simple Baseline Hong Liu AAML 24 0 0 13 Jun 2023
Annealing Self-Distillation Rectification Improves Adversarial Training Yuehua Wu Hung-Jui Wang Shang-Tse Chen AAML 24 3 0 20 May 2023
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses Joana Cabral Costa Tiago Roxo Hugo Manuel Proença Pedro R. M. Inácio AAML 37 49 0 18 May 2023
Overload: Latency Attacks on Object Detection for Edge Devices Erh-Chung Chen Pin-Yu Chen I-Hsin Chung Che-Rung Lee AAML 36 12 0 11 Apr 2023
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm Bakary Badjie José Cecílio António Casimiro AAML 14 3 0 28 Mar 2023
DISCO: Adversarial Defense with Local Implicit Functions Chih-Hui Ho Nuno Vasconcelos AAML 21 38 0 11 Dec 2022
Robust Models are less Over-Confident Julia Grabinski Paul Gavrikov J. Keuper M. Keuper AAML 28 24 0 12 Oct 2022
Diversified Adversarial Attacks based on Conjugate Gradient Method Keiichiro Yamamura Haruki Sato Nariaki Tateiwa Nozomi Hata Toru Mitsutake Issa Oe Hiroki Ishikura Katsuki Fujisawa AAML 14 14 0 20 Jun 2022
Adversarial Robustness through the Lens of Convolutional Filters Paul Gavrikov J. Keuper 30 15 0 05 Apr 2022
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 219 676 0 19 Oct 2020
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization Saehyung Lee Hyungyu Lee Sungroh Yoon AAML 158 113 0 05 Mar 2020
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 261 3,109 0 04 Nov 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 281 5,833 0 08 Jul 2016