https://github.com/hichemdebbi/causadv
공식 코드CausAdv: A Causal-based Framework for Detecting Adversarial Examples
Papers with Code 아카이브
Adversarial Robustness
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 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/hjk92g/proper_measure_robustness
공식 코드Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary
https://github.com/hongsinlee/flair
공식 코드Enhancing Robustness in Incremental Learning with Adversarial Training
https://github.com/hongw579/agkd-bml
공식 코드https://github.com/hongyanz/TRADES-smoothing
공식 코드Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images
https://github.com/hopl1t/vub
공식 코드Tighter Bounds on the Information Bottleneck with Application to Deep Learning
https://github.com/hrcheng1066/awesome-pruning
공식 코드A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations
https://github.com/hsndbk4/arc
공식 코드https://github.com/huangd1999/bort
공식 코드Two Heads are Better than One: Robust Learning Meets Multi-branch Models
https://github.com/huanranchen/AdversarialAttacks
공식 코드https://github.com/huanranchen/diffusionclassifier
공식 코드https://github.com/ibm/great-score
공식 코드GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models
https://github.com/ieslab-ynu/EWAS
공식 코드Improving Robustness of Convolutional Neural Networks Using Element-Wise Activation Scaling
https://github.com/igitugraz/robustsnnconversion
공식 코드Adversarially Robust Spiking Neural Networks Through Conversion
https://github.com/igor-buzhinsky/latent-space-nn-evaluation
공식 코드Metrics and methods for robustness evaluation of neural networks with generative models
https://github.com/illidanlab/FedRBN
공식 코드Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learning
https://github.com/inspire-group/proxy-distributions
공식 코드Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
https://github.com/inspire-group/robustness-via-transport
공식 코드Lower Bounds on Adversarial Robustness from Optimal Transport
https://github.com/intelligent-computing-lab-yale/rate-vs-direct
공식 코드https://github.com/ivandariojr/lyapunovlearning
공식 코드https://github.com/izzayacine/SwiftXPlain
공식 코드https://github.com/jaekyeom/drop-bottleneck
공식 코드Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration
https://github.com/jaewonalive/peeraid
공식 코드PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor
https://github.com/james-simon/eigenlearning
공식 코드https://github.com/jasonfu1998/diffpad
공식 코드DiffPAD: Denoising Diffusion-based Adversarial Patch Decontamination
https://github.com/jayjaynandy/RBF-CNN
공식 코드Approximate Manifold Defense Against Multiple Adversarial Perturbations
https://github.com/jeffreyzpan/adversarial-playground
공식 코드On Intrinsic Dataset Properties for Adversarial Machine Learning
https://github.com/jeromerony/adversarial-library
공식 코드Mind the box: $l_1$-APGD for sparse adversarial attacks on image classifiers
https://github.com/jfc43/eval-transductive-robustness
공식 코드Towards Evaluating the Robustness of Neural Networks Learned by Transduction
https://github.com/jfc43/stratified-adv-rej
공식 코드https://github.com/jh-jeong/nuisance_ib
공식 코드Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck
https://github.com/jh-jeong/smoothing-consistency
공식 코드Consistency Regularization for Certified Robustness of Smoothed Classifiers
https://github.com/jh-jeong/smoothing-multiscale
공식 코드https://github.com/jia-kai/eevbnn
공식 코드https://github.com/jiamingzhang94/adversarial-prompt-tuning
공식 코드https://github.com/jiangllan/rose
공식 코드ROSE: Robust Selective Fine-tuning for Pre-trained Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.