Using Pre-Training Can Improve Model Robustness and Uncertainty

28 January 2019

Papers citing "Using Pre-Training Can Improve Model Robustness and Uncertainty"

50 / 146 papers shown

Title
Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients Xiuwen Fang Mang Ye Bo Du FedML 74 1 0 12 Mar 2025
Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery Shuyi Jia Shitij Govil Manav Ramprasad Victor Fung AI4CE 64 1 0 03 Mar 2025
A Large-Scale Study of Model Integration in ML-Enabled Software Systems Yorick Sens Henriette Knopp Sven Peldszus Thorsten Berger AIFin 31 2 0 12 Aug 2024
Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers Jonas Ngnawé Sabyasachi Sahoo Y. Pequignot Frédéric Precioso Christian Gagné AAML 42 0 0 26 Jun 2024
Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape Tiejin Chen Wenwang Huang Linsey Pang Dongsheng Luo Hua Wei OOD 49 0 0 09 Mar 2024
FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning Jie Shen Shusen Yang Cong Zhao Xuebin Ren Peng Zhao Yuqian Yang Qing Han Shuaijun Wu FedML 22 9 0 29 Dec 2023
MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness Xiaoyun Xu Shujian Yu Jingzheng Wu S. Picek AAML 35 0 0 08 Dec 2023
xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data Jing Gong Minsheng Hao Xingyi Cheng Xin Zeng Chiming Liu Jianzhu Ma Xuegong Zhang Taifeng Wang Leo T. Song 31 17 0 26 Nov 2023
Temperature-scaling surprisal estimates improve fit to human reading times -- but does it do so for the "right reasons"? Tong Liu Iza vSkrjanec Vera Demberg 48 5 0 15 Nov 2023
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method Yukun Zhao Lingyong Yan Weiwei Sun Guoliang Xing Chong Meng Shuaiqiang Wang Zhicong Cheng Zhaochun Ren Dawei Yin 29 35 0 27 Oct 2023
Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning Lucas Weber Elia Bruni Dieuwke Hupkes 32 24 0 20 Oct 2023
SynthTab: Leveraging Synthesized Data for Guitar Tablature Transcription Yongyi Zang Yi Zhong Frank Cwitkowitz Zhiyao Duan 29 6 0 16 Sep 2023
Position: Key Claims in LLM Research Have a Long Tail of Footnotes Anna Rogers A. Luccioni 53 19 0 14 Aug 2023
COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts Xiaofeng Mao YueFeng Chen Yao Zhu Da Chen Hang Su Rong Zhang H. Xue ObjD OOD 38 18 0 24 Jul 2023
Long-Tailed Continual Learning For Visual Food Recognition Jiangpeng He Luotao Lin Jack Ma H. Eicher-Miller F. Zhu Fengqing M Zhu 59 14 0 01 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
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability Jianing Zhu Hengzhuang Li Jiangchao Yao Tongliang Liu Jianliang Xu Bo Han OODD 43 12 0 06 Jun 2023
Selection for short-term empowerment accelerates the evolution of homeostatic neural cellular automata Caitlin Grasso Josh Bongard 31 0 0 24 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 40 50 0 18 May 2023
On Efficient Training of Large-Scale Deep Learning Models: A Literature Review Li Shen Yan Sun Zhiyuan Yu Liang Ding Xinmei Tian Dacheng Tao VLM 30 41 0 07 Apr 2023
PIAT: Parameter Interpolation based Adversarial Training for Image Classification Kun He Xin Liu Yichen Yang Zhou Qin Weigao Wen Hui Xue J. Hopcroft AAML 30 0 0 24 Mar 2023
Randomized Adversarial Training via Taylor Expansion Gao Jin Xinping Yi Dengyu Wu Ronghui Mu Xiaowei Huang AAML 44 34 0 19 Mar 2023
Overwriting Pretrained Bias with Finetuning Data Angelina Wang Olga Russakovsky 26 29 0 10 Mar 2023
Certified Robust Neural Networks: Generalization and Corruption Resistance Amine Bennouna Ryan Lucas Bart P. G. Van Parys 38 10 0 03 Mar 2023
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking Chang-Shu Liu Yinpeng Dong Wenzhao Xiang X. Yang Hang Su Junyi Zhu YueFeng Chen Yuan He H. Xue Shibao Zheng OOD VLM AAML 33 72 0 28 Feb 2023
Differentially Private Diffusion Models Generate Useful Synthetic Images Sahra Ghalebikesabi Leonard Berrada Sven Gowal Ira Ktena Robert Stanforth Jamie Hayes Soham De Samuel L. Smith Olivia Wiles Borja Balle DiffM 31 69 0 27 Feb 2023
Better Diffusion Models Further Improve Adversarial Training Zekai Wang Tianyu Pang Chao Du Min-Bin Lin Weiwei Liu Shuicheng Yan DiffM 24 208 0 09 Feb 2023
Revisiting Pre-training in Audio-Visual Learning Ruoxuan Feng Wenke Xia Di Hu 32 1 0 07 Feb 2023
Data Augmentation Alone Can Improve Adversarial Training Lin Li Michael W. Spratling 16 50 0 24 Jan 2023
A Stability Analysis of Fine-Tuning a Pre-Trained Model Z. Fu Anthony Man-Cho So Nigel Collier 23 3 0 24 Jan 2023
Uncertainty Estimation based on Geometric Separation Gabriella Chouraqui L. Cohen Gil Einziger Liel Leman 30 0 0 11 Jan 2023
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks Nikolaos Antoniou Efthymios Georgiou Alexandros Potamianos AAML 29 5 0 15 Dec 2022
MixBoost: Improving the Robustness of Deep Neural Networks by Boosting Data Augmentation Zhendong Liu Wenyu Jiang Min Guo Chongjun Wang AAML 21 1 0 08 Dec 2022
Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimates Dongfang Li Baotian Hu Qingcai Chen 16 8 0 06 Nov 2022
Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting Peng-Fei Hou Jie Han Xingyu Li AAML OOD 23 11 0 26 Oct 2022
Robust Models are less Over-Confident Julia Grabinski Paul Gavrikov J. Keuper M. Keuper AAML 36 24 0 12 Oct 2022
Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goals Rohin Shah Vikrant Varma Ramana Kumar Mary Phuong Victoria Krakovna J. Uesato Zachary Kenton 37 68 0 04 Oct 2022
Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training Saehyung Lee Hyungyu Lee AAML 29 2 0 27 Sep 2022
A Light Recipe to Train Robust Vision Transformers Edoardo Debenedetti Vikash Sehwag Prateek Mittal ViT 32 68 0 15 Sep 2022
Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift Ananya Kumar Tengyu Ma Percy Liang Aditi Raghunathan UQCV OODD OOD 42 38 0 18 Jul 2022
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning Tianlong Chen Sijia Liu Shiyu Chang Lisa Amini Zhangyang Wang CLL 26 4 0 15 Jun 2022
X-Risk Analysis for AI Research Dan Hendrycks Mantas Mazeika 33 67 0 13 Jun 2022
Revealing Single Frame Bias for Video-and-Language Learning Jie Lei Tamara L. Berg Joey Tianyi Zhou 24 111 0 07 Jun 2022
Post-hoc Concept Bottleneck Models Mert Yuksekgonul Maggie Wang James Zou 145 185 0 31 May 2022
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling Lei Cheng Feng Yin Sergios Theodoridis S. Chatzis Tsung-Hui Chang 68 75 0 28 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
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness J. Liu Shreyas Padhy Jie Jessie Ren Zi Lin Yeming Wen Ghassen Jerfel Zachary Nado Jasper Snoek Dustin Tran Balaji Lakshminarayanan UQCV BDL 26 48 0 01 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
A Comparative Survey of Deep Active Learning Xueying Zhan Qingzhong Wang Kuan-Hao Huang Haoyi Xiong Dejing Dou Antoni B. Chan FedML HAI 24 105 0 25 Mar 2022