https://github.com/bymavis/CAS_ICLR2021
공식 코드Improving Adversarial Robustness via Channel-wise Activation Suppressing
Papers with Code 아카이브
Adversarial Robustness
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 33
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/bymavis/adv_weight_neurips2021
공식 코드https://github.com/c0ldstudy/pointsecguard
공식 코드On Adversarial Robustness of Point Cloud Semantic Segmentation
https://github.com/cantankerousdolphin/robust-learned-representations
공식 코드Adversarial Robustness as a Prior for Learned Representations
https://github.com/captaincuong/robusttext_acl2024
공식 코드https://github.com/cassidylaidlaw/perceptual-advex
공식 코드Perceptual Adversarial Robustness: Defense Against Unseen Threat Models
https://github.com/cc233/calfat
공식 코드CalFAT: Calibrated Federated Adversarial Training with Label Skewness
https://github.com/cdluminate/robdml
공식 코드https://github.com/cdluminate/robrank
공식 코드https://github.com/centerforaisafety/adversarial-corruptions
공식 코드https://github.com/cetmann/robustness-interpretability
공식 코드On the Connection Between Adversarial Robustness and Saliency Map Interpretability
https://github.com/cfinlay/tulip
공식 코드Scaleable input gradient regularization for adversarial robustness
https://github.com/chang-george-dong/kan-for-tsc
공식 코드Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust Analysis
https://github.com/changzhang777/AFD
공식 코드AFD: Mitigating Feature Gap for Adversarial Robustness by Feature Disentanglement
https://github.com/changzhang777/ancra
공식 코드Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion Criteria
https://github.com/chaojianyu/understanding-robust-overfitting
공식 코드Understanding Robust Overfitting of Adversarial Training and Beyond
https://github.com/chasesecurity/spamdam
공식 코드SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection
https://github.com/chawins/adv-part-model
공식 코드https://github.com/chen37058/physical-attacks-in-embodied-nav
공식 코드Towards Physically Realizable Adversarial Attacks in Embodied Vision Navigation
https://github.com/chen37058/physical-attacks-in-embodied-navigation
공식 코드Towards Physically Realizable Adversarial Attacks in Embodied Vision Navigation
https://github.com/chenboluo/adversarial-defense
공식 코드Improving Adversarial Robustness via Decoupled Visual Representation Masking
https://github.com/chengleileilei/shillingrec
공식 코드Towards Robust Recommendation: A Review and an Adversarial Robustness Evaluation Library
https://github.com/chengxuduan/advhmp
공식 코드Physics-constrained Attack against Convolution-based Human Motion Prediction
https://github.com/chenwu98/agent-attack
공식 코드https://github.com/choidae1/robustify-t2i
공식 코드Adversarial Robustification via Text-to-Image Diffusion Models
https://github.com/chris-hzc/rung
공식 코드https://github.com/chs20/robustvlm
공식 코드On the Adversarial Robustness of Multi-Modal Foundation Models
https://github.com/chung-neuroai-lab/adversarial-manifolds
공식 코드Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception
https://github.com/cihangxie/SmoothAdversarialTraining
공식 코드https://github.com/cinofix/sigma-zero-adversarial-attack
공식 코드$σ$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples
https://github.com/claws-lab/few-shot-adversarial-robustness
공식 코드Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding
https://github.com/cleverhans-lab/cleverhans
공식 코드Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff
https://github.com/cmhcbb/attackbox
공식 코드https://github.com/cmu-enyac/Renofeation
공식 코드Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness
https://github.com/cmu-sei/smoothness-and-recognizability
공식 코드On the human-recognizability phenomenon of adversarially trained deep image classifiers
https://github.com/columbia/MTRobust
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.