Papers Non-Adversarial Robustness
“Non-Adversarial Robustness” 태그가 달린 논문 6편 · 필터 해제
Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation
Fully supervised deep learning (DL) models for surgical video segmentation have been shown to struggle with non-adversarial, real-world corruptions of image quality including smoke, bleeding, and low illumination. Founda…
Adversarial RobustnessImage SegmentationInteractive SegmentationNon-Adversarial Robustness+5SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge
Surgical data science has seen rapid advancement due to the excellent performance of end-to-end deep neural networks (DNNs) for surgical video analysis. Despite their successes, end-to-end DNNs have been proven susceptib…
Adversarial RobustnessData AugmentationNon-Adversarial RobustnessSegmentationTowards Robust Recommendation: A Review and an Adversarial Robustness Evaluation Library
Recently, recommender system has achieved significant success. However, due to the openness of recommender systems, they remain vulnerable to malicious attacks. Additionally, natural noise in training data and issues suc…
Adversarial RobustnessNon-Adversarial RobustnessRecommendation SystemsInput Convex Lipschitz RNN: A Fast and Robust Approach for Engineering Tasks
Computational efficiency and robustness are essential in process modeling, optimization, and control for real-world engineering applications. While neural network-based approaches have gained significant attention in rec…
Chemical ProcessComputational EfficiencyModel Predictive ControlNon-Adversarial Robustness+1Non-adversarial Robustness of Deep Learning Methods for Computer Vision
Non-adversarial robustness, also known as natural robustness, is a property of deep learning models that enables them to maintain performance even when faced with distribution shifts caused by natural variations in data.…
Adversarial RobustnessDeep LearningNon-Adversarial RobustnessA Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?
Deep neural networks for computer vision are deployed in increasingly safety-critical and socially-impactful applications, motivating the need to close the gap in model performance under varied, naturally occurring imagi…
Adversarial RobustnessData AugmentationNon-Adversarial Robustness