General Robustness

General robustness in machine learning aims to develop models resilient to various forms of unexpected input, including adversarial attacks, out-of-distribution data, and common corruptions. Current research focuses on improving robustness through techniques like adversarial training, randomized smoothing, and model editing, often applied to architectures such as convolutional neural networks and visual state space models. This pursuit is crucial for deploying reliable and trustworthy AI systems in real-world applications, where models must handle unpredictable and potentially malicious inputs, improving the safety and dependability of AI across various domains.

Papers

October 8, 2024

MIRACLE 3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model construction
Hossein Resani, Behrooz Nasihatkon
Point Cloud Continual LEArning Shape Aware 3D Object Classification General Robustness Shape Model 3D Domain Learning Privacy

September 12, 2024

FedProphet: Memory-Efficient Federated Adversarial Training via Theoretic-Robustness and Low-Inconsistency Cascade Learning
Minxue Tang, Yitu Wang, Jingyang Zhang, Louis DiValentin, Aolin Ding, Amin Hass, Yiran Chen, Hai "Helen" Li
Adversarial Robustness General Robustness Federated Adversarial Cascade Learning

May 28, 2024

Towards Unified Robustness Against Both Backdoor and Adversarial Attacks
Zhenxing Niu, Yuyao Sun, Qiguang Miao, Rong Jin, Gang Hua
Adversarial Attack Adversarial Robustness Backdoor Attack Adversarial Defense General Robustness

May 23, 2024

Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolution
Zakariya Chaouai, Mohamed Tamaazousti
Super Resolution Image Super Resolution Randomized Smoothing Image Corruption Low Resolution Image General Robustness Real World Super Resolution

April 8, 2024

Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey
Naveen Karunanayake, Ravin Gunawardena, Suranga Seneviratne, Sanjay Chawla
Timely Survey Adversarial Attack Adversarial Example Adversarial Robustness Distribution Detection Distribution Data General Robustness

March 16, 2024

Understanding Robustness of Visual State Space Models for Image Classification
Chengbin Du, Yanxi Li, Chang Xu
Adversarial Attack Native Robustness Adversarial Robustness Image Classification Adversarial Patch Attack Visual State Space Model General Robustness

February 17, 2024

Maintaining Adversarial Robustness in Continuous Learning
Xiaolei Ru, Xiaowei Cao, Zijia Liu, Jack Murdoch Moore, Xin-Ya Zhang, Xia Zhu, Wenjia Wei, Gang Yan
Adversarial Attack Adversarial Robustness Continuous Learning General Robustness Robust Continual Learning

November 29, 2023

Robustness Approaches for the Examination Timetabling Problem under Data Uncertainty
Bernd Bassimir, Rolf Wanka
Robust Optimization Data Uncertainty General Robustness Instance Generation Examination Timetabling Problem

October 21, 2023

Training Image Derivatives: Increased Accuracy and Universal Robustness
Vsevolod I. Avrutskiy
Robustness Analysis General Robustness Robustness Problem Image Derivative

September 16, 2023

Unifying Robustness and Fidelity: A Comprehensive Study of Pretrained Generative Methods for Speech Enhancement in Adverse Conditions
Heming Wang, Meng Yu, Hao Zhang, Chunlei Zhang, Zhongweiyang Xu, Muqiao Yang, Yixuan Zhang, Dong Yu
Pre Trained Speech Enhancement Speech Processing Speech Signal Speech Quality Pre Trained Generative Model Adverse Condition High Quality Speech Fidelity Reward General Robustness

May 15, 2023

Exploiting Frequency Spectrum of Adversarial Images for General Robustness
Chun Yang Tan, Kazuhiko Kawamoto, Hiroshi Kera
Adversarial Perturbation Adversarial Image General Robustness Adversarial Covariate Shift

February 28, 2023

Edit at your own risk: evaluating the robustness of edited models to distribution shifts
Davis Brown, Charles Godfrey, Cody Nizinski, Jonathan Tu, Henry Kvinge
Native Robustness Distribution Shift High Quality Risk Description Model Editing General Robustness Edited Model

February 24, 2023

Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?
Ruisi Cai, Zhenyu Zhang, Zhangyang Wang
Native Robustness Model Robustness Patch Based Robust Model Weight Freezing General Robustness

October 13, 2022

An Embarrassingly Simple Backdoor Attack on Self-supervised Learning
Changjiang Li, Ren Pang, Zhaohan Xi, Tianyu Du, Shouling Ji, Yuan Yao, Ting Wang
Self Supervised Learning Adversarial Robustness Backdoor Attack Backdoor Learning General Robustness

March 14, 2022

Adversarial amplitude swap towards robust image classifiers
Chun Yang Tan, Kazuhiko Kawamoto, Hiroshi Kera
Native Robustness Image Classification Adversarial Perturbation Adversarial Image Robust Overfitting General Robustness Adversarial Covariate Shift

January 6, 2022

Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification
Saptarshi Chakraborty, Debolina Paul, Swagatam Das
General Analysis Robust Estimation Model Misspecification Fast Rate Robust Prediction General Robustness Linear Predictor Robust Statistic

November 18, 2021

Wiggling Weights to Improve the Robustness of Classifiers
Sadaf Gulshad, Ivan Sosnovik, Arnold Smeulders
Native Robustness Simple Classifier Balancing Weight General Robustness Convolution Augmented Transformer

November 3, 2021

Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness
Ke Sun, Mingjie Li, Zhouchen Lin
Adversarial Robustness General Robustness Temporal Robustness Parameter Aware Robustness