Self-Adaptive Training: beyond Empirical Risk Minimization

24 February 2020

Papers citing "Self-Adaptive Training: beyond Empirical Risk Minimization"

37 / 37 papers shown

Title
Interpretable and Fair Mechanisms for Abstaining Classifiers Daphne Lenders Andrea Pugnana Roberto Pellungrini Toon Calders D. Pedreschi F. Giannotti FaML 91 1 0 24 Mar 2025
Sample Selection via Contrastive Fragmentation for Noisy Label Regression C. Kim Sangwoo Moon Jihwan Moon Dongyeon Woo Gunhee Kim NoLa 57 0 0 25 Feb 2025
Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference Jiayi Huang Sangwoo Park Osvaldo Simeone 108 2 0 03 Jan 2025
A Causal Framework for Evaluating Deferring Systems Filippo Palomba Andrea Pugnana Jose M. Alvarez Salvatore Ruggieri CML 51 1 0 29 May 2024
Learning with Noisy Labels: Interconnection of Two Expectation-Maximizations Heewon Kim Hyun Sung Chang Kiho Cho Jaeyun Lee Bohyung Han NoLa 26 2 0 09 Jan 2024
Learning to Abstain From Uninformative Data Yikai Zhang Songzhu Zheng M. Dalirrooyfard Pengxiang Wu Anderson Schneider Anant Raj Yuriy Nevmyvaka Chao Chen 26 2 0 25 Sep 2023
Omnipotent Adversarial Training in the Wild Guanlin Li Kangjie Chen Yuan Xu Han Qiu Tianwei Zhang 26 0 0 14 Jul 2023
Group-based Robustness: A General Framework for Customized Robustness in the Real World Weiran Lin Keane Lucas Neo Eyal Lujo Bauer Michael K. Reiter Mahmood Sharif OOD AAML 27 1 0 29 Jun 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 40 50 0 18 May 2023
CAT:Collaborative Adversarial Training Xingbin Liu Huafeng Kuang Xianming Lin Yongjian Wu Rongrong Ji AAML 22 4 0 27 Mar 2023
Gumbel-Softmax Selective Networks Mahmoud Salem Mohamed Osama Ahmed Frederick Tung Gabriel L. Oliveira 11 1 0 19 Nov 2022
AUC-based Selective Classification Andrea Pugnana Salvatore Ruggieri 23 9 0 19 Oct 2022
Robust Models are less Over-Confident Julia Grabinski Paul Gavrikov J. Keuper M. Keuper AAML 36 24 0 12 Oct 2022
CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media Momchil Hardalov Anton Chernyavskiy Ivan Koychev Dmitry Ilvovsky Preslav Nakov HAI 35 15 0 10 Oct 2022
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance Bahjat Kawar Roy Ganz Michael Elad DiffM 26 38 0 18 Aug 2022
Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels Chuang Zhang Li Shen Jian Yang Chen Gong NoLa 27 5 0 27 Jun 2022
Wavelet Regularization Benefits Adversarial Training Jun Yan Huilin Yin Xiaoyang Deng Zi-qin Zhao Wancheng Ge Hao Zhang Gerhard Rigoll AAML 19 2 0 08 Jun 2022
Selective Classification Via Neural Network Training Dynamics Stephan Rabanser Anvith Thudi Kimia Hamidieh Adam Dziedzic Nicolas Papernot 29 21 0 26 May 2022
CE-based white-box adversarial attacks will not work using super-fitting Youhuan Yang Lei Sun Leyu Dai Song Guo Xiuqing Mao Xiaoqin Wang Bayi Xu AAML 34 0 0 04 May 2022
FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning Sangmook Kim Wonyoung Shin Soohyuk Jang Hwanjun Song Se-Young Yun 31 2 0 03 May 2022
Adversarial Robustness through the Lens of Convolutional Filters Paul Gavrikov J. Keuper 38 15 0 05 Apr 2022
CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters Paul Gavrikov J. Keuper AAML 24 31 0 29 Mar 2022
Better Supervisory Signals by Observing Learning Paths Yi Ren Shangmin Guo Danica J. Sutherland 33 21 0 04 Mar 2022
Robust Training under Label Noise by Over-parameterization Sheng Liu Zhihui Zhu Qing Qu Chong You NoLa OOD 27 106 0 28 Feb 2022
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training Tu Bui Trung Le Quan Hung Tran He Zhao Dinh Q. Phung AAML OOD 31 42 0 27 Feb 2022
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training Chen Liu Zhichao Huang Mathieu Salzmann Tong Zhang Sabine Süsstrunk AAML 23 13 0 14 Dec 2021
Data Augmentation Can Improve Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 17 270 0 09 Nov 2021
Improved Regularization and Robustness for Fine-tuning in Neural Networks Dongyue Li Hongyang R. Zhang NoLa 55 54 0 08 Nov 2021
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks Maksym Yatsura J. H. Metzen Matthias Hein OOD 26 14 0 02 Nov 2021
Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification Jinghan Sun Dong Wei Kai Ma Liansheng Wang Yefeng Zheng 27 8 0 09 Oct 2021
Adversarial Robustness via Fisher-Rao Regularization Marine Picot Francisco Messina Malik Boudiaf Fabrice Labeau Ismail Ben Ayed Pablo Piantanida AAML 23 23 0 12 Jun 2021
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks Leo Schwinn René Raab A. Nguyen Dario Zanca Bjoern M. Eskofier AAML 14 58 0 21 May 2021
LAFEAT: Piercing Through Adversarial Defenses with Latent Features Yunrui Yu Xitong Gao Chengzhong Xu AAML FedML 33 44 0 19 Apr 2021
Fixing Data Augmentation to Improve Adversarial Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 36 269 0 02 Mar 2021
Understanding the Interaction of Adversarial Training with Noisy Labels Jianing Zhu Jingfeng Zhang Bo Han Tongliang Liu Gang Niu Hongxia Yang Mohan S. Kankanhalli Masashi Sugiyama AAML 27 27 0 06 Feb 2021
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification A. Aksoy Mahdyar Ravanbakhsh Begüm Demir 32 24 0 19 Dec 2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 17 323 0 07 Oct 2020