https://github.com/microsoft/distance-learner
공식 코드Distance Learner: Incorporating Manifold Prior to Model Training
Papers with Code 아카이브
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/microsoft/robustlearn
공식 코드On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
https://github.com/mikhailpautov/cccert
공식 코드CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks
https://github.com/milabhitsz/2022zhangncen
공식 코드Defending Adversarial Examples by Negative Correlation Ensemble
https://github.com/minicheshire/deq-regulating-neural-dynamics
공식 코드Improving Adversarial Robustness of DEQs with Explicit Regulations Along the Neural Dynamics
https://github.com/minicheshire/deq-white-box-robustness
공식 코드A Closer Look at the Adversarial Robustness of Deep Equilibrium Models
https://github.com/minkyu-choi04/rs-rnn
공식 코드Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises
https://github.com/mjamroz90/dnn-class-fitting
공식 코드Neural Representations Reveal Distinct Modes of Class Fitting in Residual Convolutional Networks
https://github.com/mkt1412/testtime-smoothing-defense
공식 코드An Adaptive View of Adversarial Robustness from Test-time Smoothing Defense
https://github.com/mkyucel/hybrid_augment
공식 코드HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness
https://github.com/mlech26l/quantization_aware_ibp
공식 코드https://github.com/mlgroupjlu/mixupmodelmerge
공식 코드Mixup Model Merge: Enhancing Model Merging Performance through Randomized Linear Interpolation
https://github.com/mo666666/when-adversarial-training-meets-vision-transformers
공식 코드When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture
https://github.com/momo1986/adversarialwavelettraining
공식 코드https://github.com/momo1986/robust_sam_iv
공식 코드Segment-Anything Models Achieve Zero-shot Robustness in Autonomous Driving
https://github.com/mostafa-ja/sigma-binary
공식 코드Evaluating the Robustness of Adversarial Defenses in Malware Detection Systems
https://github.com/msu-video-group/adversarial-defenses-for-iqa
공식 코드https://github.com/msu-video-group/msu_metrics_robustness_benchmark
공식 코드https://github.com/msu-video-group/nic-robustbench
공식 코드https://github.com/mtandhj/rcm
공식 코드Understanding Adversarial Robustness from Feature Maps of Convolutional Layers
https://github.com/naver-ai/hmix-gmix
공식 코드A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective
https://github.com/nesl/adversarial_genattack
공식 코드GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
https://github.com/neu-spiral/hbar
공식 코드Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness
https://github.com/neu-spiral/pwoa
공식 코드Pruning Adversarially Robust Neural Networks without Adversarial Examples
https://github.com/neuralsec/camp-robust-rl
공식 코드CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius Maximization
https://github.com/niklasrisse/adversarial-examples-and-where-to-find-them
공식 코드How to compare adversarial robustness of classifiers from a global perspective
https://github.com/niklasrisse/how-to-compare-adversarial-robustness-of-classifiers-from-a-global-perspective
공식 코드How to compare adversarial robustness of classifiers from a global perspective
https://github.com/nikolasmorshuis/advrec
공식 코드Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations
https://github.com/nilakshdas/skelevision
공식 코드SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning
https://github.com/nmndeep/robust-segmentation
공식 코드Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models
https://github.com/oodrobustbench/oodrobustbench
공식 코드https://github.com/opengvlab/stm-evaluation
공식 코드Demystify Transformers & Convolutions in Modern Image Deep Networks
https://github.com/optml-group/diffusion-mu-attack
공식 코드https://github.com/optml-group/qf-attack
공식 코드A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion
https://github.com/optml-group/robust-moe-cnn
공식 코드Robust Mixture-of-Expert Training for Convolutional Neural Networks
https://github.com/panda1230/Adversarial_NoiseLearning_NoL
공식 코드Implicit Generative Modeling of Random Noise during Training for Adversarial Robustness
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.