科研篇(11)：ICLR20 分类整理-对抗样本&Meta-Learning
文章目录
- 一、对抗样本
- 1.1Enhancing Transformation-Based Defenses Against Adversarial Attacks with a Distribution Classifier .
- 1.2 Implicit Bias of Gradient Descent based Adversarial Training on Separable Data
- 1.3 Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks
- 1.4 Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
- 1.5 Robust Local Features for Improving the Generalization of Adversarial Training
- 1.6 Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking
- 1.7 Improving Adversarial Robustness Requires Revisiting Misclassified Examples
- 1.8 Adversarial Policies: Attacking Deep Reinforcement Learning
- 1.9 Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions
- 1.10 GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification
- 1.11 Black-Box Adversarial Attack with Transferable Model-based Embedding
- 1.12 Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing
- 1.13 Adversarially Robust Representations with Smooth Encoders
- 1.14 Unpaired Point Cloud Completion on Real Scans using Adversarial Training
- 1.15 Adversarially robust transfer learning
- 1.16 Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
- 1.17 Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
- 1.18 Fast is better than free: Revisiting adversarial training
- 1.19 Intriguing Properties of Adversarial Training at Scale
- 1.20 Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks
- 1.21 Jacobian Adversarially Regularized Networks for Robustness
- 1.22 Certified Defenses for Adversarial Patches
- 1.23 Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks
- 1.24 Provable robustness against all adversarial lp-perturbations for p ≥ 1
- 1.25 EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness Against Adversarial Attacks
- 1.26 MMA Training: Direct Input Space Margin Maximization through Adversarial Training
- 1.27 BayesOpt Adversarial Attack
- 1.28 Unrestricted Adversarial Examples via Semantic Manipulation
- 1.29 BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES
- 1.30 (Spotlight)Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets
- 1.31 (Spotlight)Enhancing Adversarial Defense by k-Winners-Take-All
- 1.32 (Spotlight)FreeLB: Enhanced Adversarial Training for Natural Language Understanding
- 1.33 (Spotlight)ON ROBUSTNESS OF NEURAL ORDINARY DIFFERENTIAL EQUATIONS
- 1.34 (Oral)Adversarial Training and Provable Defenses: Bridging the Gap
- 1.35 MACER: ATTACK-FREE AND SCALABLE ROBUST TRAINING VIA MAXIMIZING CERTIFIED RADIUS
- 1.36 IMPROVED SAMPLE COMPLEXITIES FOR DEEP NETWORKS AND ROBUST CLASSIFICATION VIA AN ALLLAYER MARGIN
- 1.37 TOWARDS STABLE AND EFFICIENT TRAINING OF VERIFIABLY ROBUST NEURAL NETWORKS
- 1.38 TRIPLE WINS: BOOSTING ACCURACY, ROBUSTNESS AND EFFICIENCY TOGETHER BY ENABLING INPUTADAPTIVE INFERENCE
- 1.39 A FRAMEWORK FOR ROBUSTNESS CERTIFICATION OF SMOOTHED CLASSIFIERS USING F-DIVERGENCES
- 1.40 ROBUSTNESS VERIFICATION FOR TRANSFORMERS
一、对抗样本
1.1Enhancing Transformation-Based Defenses Against Adversarial Attacks with a Distribution Classifier .
PAPER LINK
1.2 Implicit Bias of Gradient Descent based Adversarial Training on Separable Data
PAPER LINK
1.3 Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks
PAPER LINK
1.4 Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
PAPER LINK
1.5 Robust Local Features for Improving the Generalization of Adversarial Training
PAPER LINK
1.6 Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking
PAPER LINK
1.7 Improving Adversarial Robustness Requires Revisiting Misclassified Examples
PAPER LINK
1.8 Adversarial Policies: Attacking Deep Reinforcement Learning
PAPER LINK
1.9 Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions
PAPER LINK
1.10 GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification
PAPER LINK
1.11 Black-Box Adversarial Attack with Transferable Model-based Embedding
PAPER LINK
1.12 Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing
PAPER LINK
1.13 Adversarially Robust Representations with Smooth Encoders
PAPER LINK
1.14 Unpaired Point Cloud Completion on Real Scans using Adversarial Training
PAPER LINK
1.15 Adversarially robust transfer learning
PAPER LINK
1.16 Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
PAPER LINK
1.17 Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
PAPER LINK
1.18 Fast is better than free: Revisiting adversarial training
PAPER LINK
1.19 Intriguing Properties of Adversarial Training at Scale
PAPER LINK
1.20 Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks
PAPER LINK
1.21 Jacobian Adversarially Regularized Networks for Robustness
PAPER LINK
1.22 Certified Defenses for Adversarial Patches
PAPER LINK
1.23 Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks
PAPER LINK
1.24 Provable robustness against all adversarial lp-perturbations for p ≥ 1
PAPER LINK
1.25 EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness Against Adversarial Attacks
PAPER LINK
1.26 MMA Training: Direct Input Space Margin Maximization through Adversarial Training
PAPER LINK
1.27 BayesOpt Adversarial Attack
PAPER LINK
1.28 Unrestricted Adversarial Examples via Semantic Manipulation
PAPER LINK
1.29 BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES
PAPER LINK
1.30 (Spotlight)Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets
PAPER LINK
1.31 (Spotlight)Enhancing Adversarial Defense by k-Winners-Take-All
PAPER LINK
1.32 (Spotlight)FreeLB: Enhanced Adversarial Training for Natural Language Understanding
PAPER LINK
1.33 (Spotlight)ON ROBUSTNESS OF NEURAL ORDINARY DIFFERENTIAL EQUATIONS
PAPER LINK
1.34 (Oral)Adversarial Training and Provable Defenses: Bridging the Gap
PAPER LINK
1.35 MACER: ATTACK-FREE AND SCALABLE ROBUST TRAINING VIA MAXIMIZING CERTIFIED RADIUS
PAPER LINK
1.36 IMPROVED SAMPLE COMPLEXITIES FOR DEEP NETWORKS AND ROBUST CLASSIFICATION VIA AN ALLLAYER MARGIN
PAPER LINK
1.37 TOWARDS STABLE AND EFFICIENT TRAINING OF VERIFIABLY ROBUST NEURAL NETWORKS
PAPER LINK
1.38 TRIPLE WINS: BOOSTING ACCURACY, ROBUSTNESS AND EFFICIENCY TOGETHER BY ENABLING INPUTADAPTIVE INFERENCE
PAPER LINK
1.39 A FRAMEWORK FOR ROBUSTNESS CERTIFICATION OF SMOOTHED CLASSIFIERS USING F-DIVERGENCES
PAPER LINK
1.40 ROBUSTNESS VERIFICATION FOR TRANSFORMERS
PAPER LINK