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Adversarial Robustness
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/IBM/composite-adv
공식 코드https://github.com/IBM/model-sanitization
공식 코드Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
https://github.com/Infernolia/WEDGE
공식 코드WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models
https://github.com/InfoLab-SKKU/black-box-attacks
공식 코드Impact of Architectural Modifications on Deep Learning Adversarial Robustness
https://github.com/JiancongXiao/Adaptive-Smoothness-Weighted-AT
공식 코드https://github.com/JiancongXiao/On-manifold-adversarial-examples
공식 코드Understanding Adversarial Robustness Against On-manifold Adversarial Examples
https://github.com/JindongGu/VoteAttack
공식 코드https://github.com/JoakimEdin/explainable-medical-coding
공식 코드An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records
https://github.com/Juintin/Bridge-FAST-and-PGD-AT
공식 코드Bridging the Performance Gap between FGSM and PGD Adversarial Training
https://github.com/KacemKhaled/model-stealing
공식 코드Careful What You Wish For: on the Extraction of Adversarially Trained Models
https://github.com/KaidiXu/GCN_ADV_Train
공식 코드Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
https://github.com/KaihuaTang/Adversarial-Robustness-by-Causal-Intervention.pytorch
공식 코드https://github.com/KaosEngineer/PriorNetworks
공식 코드Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
https://github.com/KemingWu/RSC-SNN
공식 코드https://github.com/KieranXWang/HRS
공식 코드https://github.com/KululuMi/RCL
공식 코드https://github.com/KuofengGao/MLLM-Grounding-Robustness
공식 코드Adversarial Robustness for Visual Grounding of Multimodal Large Language Models
https://github.com/LTS4/hold-me-tight
공식 코드Hold me tight! Influence of discriminative features on deep network boundaries
https://github.com/LiQiufu/WaveCNet
공식 코드WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification
https://github.com/LiYingwei/ShapeTextureDebiasedTraining
공식 코드https://github.com/LitterQ/ATLD-pytorch
공식 코드Improving Model Robustness with Latent Distribution Locally and Globally
https://github.com/LixiaoTHU/PartImageNetPP
공식 코드PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition
https://github.com/M4xim4l/InNOutRobustness
공식 코드Adversarial Robustness on In- and Out-Distribution Improves Explainability
https://github.com/MAGICS-LAB/GERM
공식 코드Fast and Low-Cost Genomic Foundation Models via Outlier Removal
https://github.com/MAGICS-LAB/GenoArmory
공식 코드GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models
https://github.com/MKYucel/adversarial_robustness_zsl
공식 코드A Deep Dive into Adversarial Robustness in Zero-Shot Learning
https://github.com/MLSpeech/CRP
공식 코드Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on Accuracy
https://github.com/MTandHJ/roboc
공식 코드An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks
https://github.com/MadryLab/adversarial_spatial
공식 코드https://github.com/MadryLab/cifar10_challenge
공식 코드Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/MadryLab/mnist_challenge
공식 코드Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/MadryLab/relu_stable
공식 코드Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability
https://github.com/MadryLab/robustness
공식 코드Adversarial Robustness as a Prior for Learned Representations
https://github.com/MadryLab/spatial-pytorch
공식 코드https://github.com/MarinePICOT/Adversarial-Robustness-via-Fisher-Rao-Regularization
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.