Initial Query

From the following data extract the best performing defense each year, in the format {year: robust accuracy} So for example the answer for {"2024": 69.71, "2023": ..., ...}, now fill it in for every other year. Return the answer as a JSON dict. Rank Method Standard accuracy AutoAttack robust accuracy Best known robust accuracy AA eval. potentially unreliable Extra data Architecture Venue 1 Robust Principles: Architectural Design Principles for Adversarially Robust CNNs It uses additional 50M synthetic images in training. 93.27% 71.07% 71.07% × × RaWideResNet-70-16 BMVC 2023 2 Better Diffusion Models Further Improve Adversarial Training It uses additional 50M synthetic images in training. 93.25% 70.69% 70.69% × × WideResNet-70-16 ICML 2023 3 MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers It uses an ensemble of networks. The robust base classifier uses 50M synthetic images. 69.71% robust accuracy is due to the original evaluation (Adaptive AutoAttack) 95.19% 70.08% 69.71% × ☑ ResNet-152 + WideResNet-70-16 arXiv, Feb 2024 4 Improving the Accuracy-Robustness Trade-off of Classifiers via Adaptive Smoothing It uses an ensemble of networks. The robust base classifier uses 50M synthetic images. 95.23% 68.06% 68.06% × ☑ ResNet-152 + WideResNet-70-16 + mixing network SIMODS 2024 5 Decoupled Kullback-Leibler Divergence Loss It uses additional 20M synthetic images in training. 92.16% 67.73% 67.73% × × WideResNet-28-10 arXiv, May 2023 6 Better Diffusion Models Further Improve Adversarial Training It uses additional 20M synthetic images in training. 92.44% 67.31% 67.31% × × WideResNet-28-10 ICML 2023 7 Fixing Data Augmentation to Improve Adversarial Robustness 66.56% robust accuracy is due to the original evaluation (AutoAttack + MultiTargeted) 92.23% 66.58% 66.56% × ☑ WideResNet-70-16 arXiv, Mar 2021 8 Improving Robustness using Generated Data It uses additional 100M synthetic images in training. 66.10% robust accuracy is due to the original evaluation (AutoAttack + MultiTargeted) 88.74% 66.11% 66.10% × × WideResNet-70-16 NeurIPS 2021 9 Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples 65.87% robust accuracy is due to the original evaluation (AutoAttack + MultiTargeted) 91.10% 65.88% 65.87% × ☑ WideResNet-70-16 arXiv, Oct 2020 10 Revisiting Residual Networks for Adversarial Robustness: An Architectural Perspective 91.58% 65.79% 65.79% × ☑ WideResNet-A4 arXiv, Dec. 2022 11 Fixing Data Augmentation to Improve Adversarial Robustness It uses additional 1M synthetic images in training. 64.58% robust accuracy is due to the original evaluation (AutoAttack + MultiTargeted) 88.50% 64.64% 64.58% × × WideResNet-106-16 arXiv, Mar 2021 12 Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks Based on the model Rebuffi2021Fixing_70_16_cutmix_extra. 64.20% robust accuracy is due to AutoAttack + transfer APGD from Rebuffi2021Fixing_70_16_cutmix_extra 93.73% 71.28% 64.20% ☑ ☑ WideResNet-70-16, Neural ODE block NeurIPS 2021 13 Fixing Data Augmentation to Improve Adversarial Robustness It uses additional 1M synthetic images in training. 64.20% robust accuracy is due to the original evaluation (AutoAttack + MultiTargeted) 88.54% 64.25% 64.20% × × WideResNet-70-16 arXiv, Mar 2021 14 Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness It uses additional 10M synthetic images in training. 93.69% 63.89% 63.89% × × WideResNet-28-10 ICLR 2023 15 Improving Robustness using Generated Data It uses additional 100M synthetic images in training. 63.38% robust accuracy is due to the original evaluation (AutoAttack + MultiTargeted) 87.50% 63.44% 63.38% × × WideResNet-28-10 NeurIPS 2021 16 Robustness and Accuracy Could Be Reconcilable by (Proper) Definition It uses additional 1M synthetic images in training. 89.01% 63.35% 63.35% × × WideResNet-70-16 ICML 2022 17 Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off 91.47% 62.83% 62.83% × ☑ WideResNet-34-10 OpenReview, Jun 2021 18 Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? It uses additional 10M synthetic images in training. 87.30% 62.79% 62.79% × × ResNest152 ICLR 2022 19 Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples 62.76% robust accuracy is due to the original evaluation (AutoAttack + MultiTargeted) 89.48% 62.80% 62.76% × ☑ WideResNet-28-10 arXiv, Oct 2020 20 Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Uses exponential moving average (EMA) 91.23% 62.54% 62.54% × ☑ WideResNet-34-R NeurIPS 2021 21 Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks 90.56% 61.56% 61.56% × ☑ WideResNet-34-R NeurIPS 2021 22 Parameterizing Activation Functions for Adversarial Robustness It uses additional ~6M synthetic images in training. 87.02% 61.55% 61.55% × × WideResNet-28-10-PSSiLU arXiv, Oct 2021 23 Robustness and Accuracy Could Be Reconcilable by (Proper) Definition It uses additional 1M synthetic images in training. 88.61% 61.04% 61.04% × × WideResNet-28-10 ICML 2022 24 Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off It uses additional 1M synthetic images in training. 88.16% 60.97% 60.97% × × WideResNet-28-10 OpenReview, Jun 2021 25 Fixing Data Augmentation to Improve Adversarial Robustness It uses additional 1M synthetic images in training. 60.73% robust accuracy is due to the original evaluation (AutoAttack + MultiTargeted) 87.33% 60.75% 60.73% × × WideResNet-28-10 arXiv, Mar 2021 26 Do Wider Neural Networks Really Help Adversarial Robustness? 87.67% 60.65% 60.65% Unknown ☑ WideResNet-34-15 arXiv, Oct 2020 27 Improving Neural Network Robustness via Persistency of Excitation 86.53% 60.41% 60.41% × ☑ WideResNet-34-15 ACC 2022 28 Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? It uses additional 10M synthetic images in training. 86.68% 60.27% 60.27% × × WideResNet-34-10 ICLR 2022 29 Adversarial Weight Perturbation Helps Robust Generalization 88.25% 60.04% 60.04% × ☑ WideResNet-28-10 NeurIPS 2020 30 Improving Neural Network Robustness via Persistency of Excitation 89.46% 59.66% 59.66% × ☑ WideResNet-28-10 ACC 2022 31 Geometry-aware Instance-reweighted Adversarial Training Uses ℓ ∞ = 0.031 ≈ 7.9/255 instead of 8/255. 89.36% 59.64% 59.64% × ☑ WideResNet-28-10 ICLR 2021 32 Unlabeled Data Improves Adversarial Robustness 89.69% 59.53% 59.53% × ☑ WideResNet-28-10 NeurIPS 2019 33 Improving Robustness using Generated Data It uses additional 100M synthetic images in training. 58.50% robust accuracy is due to the original evaluation (AutoAttack + MultiTargeted) 87.35% 58.63% 58.50% × × PreActResNet-18 NeurIPS 2021 34 Data filtering for efficient adversarial training 86.10% 58.09% 58.09% × × WideResNet-34-20 Pattern Recognition 2024 35 Scaling Adversarial Training to Large Perturbation Bounds 85.32% 58.04% 58.04% × × WideResNet-34-10 ECCV 2022 36 Efficient and Effective Augmentation Strategy for Adversarial Training 88.71% 57.81% 57.81% × × WideResNet-34-10 NeurIPS 2022 37 LTD: Low Temperature Distillation for Robust Adversarial Training 86.03% 57.71% 57.71% × × WideResNet-34-20 arXiv, Nov 2021 38 Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off 89.02% 57.67% 57.67% × ☑ PreActResNet-18 OpenReview, Jun 2021 39 LAS-AT: Adversarial Training with Learnable Attack Strategy 85.66% 57.61% 57.61% × × WideResNet-70-16 arXiv, Mar 2022 40 A Light Recipe to Train Robust Vision Transformers 91.73% 57.58% 57.58% × ☑ XCiT-L12 arXiv, Sep 2022 41 Data filtering for efficient adversarial training 86.54% 57.30% 57.30% × × WideResNet-34-10 Pattern Recognition 2024 42 A Light Recipe to Train Robust Vision Transformers 91.30% 57.27% 57.27% × ☑ XCiT-M12 arXiv, Sep 2022 43 Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples 57.14% robust accuracy is due to the original evaluation (AutoAttack + MultiTargeted) 85.29% 57.20% 57.14% × × WideResNet-70-16 arXiv, Oct 2020 44 HYDRA: Pruning Adversarially Robust Neural Networks Compressed model 88.98% 57.14% 57.14% × ☑ WideResNet-28-10 NeurIPS 2020 45 Decoupled Kullback-Leibler Divergence Loss 85.31% 57.09% 57.09% × × WideResNet-34-10 arXiv, May 2023 46 Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off It uses additional 1M synthetic images in training. 86.86% 57.09% 57.09% × × PreActResNet-18 OpenReview, Jun 2021 47 LTD: Low Temperature Distillation for Robust Adversarial Training 85.21% 56.94% 56.94% × × WideResNet-34-10 arXiv, Nov 2021 48 Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples 56.82% robust accuracy is due to the original evaluation (AutoAttack + MultiTargeted) 85.64% 56.86% 56.82% × × WideResNet-34-20 arXiv, Oct 2020 49 Fixing Data Augmentation to Improve Adversarial Robustness It uses additional 1M synthetic images in training. 83.53% 56.66% 56.66% × × PreActResNet-18 arXiv, Mar 2021 50 Improving Adversarial Robustness Requires Revisiting Misclassified Examples 87.50% 56.29% 56.29% × ☑ WideResNet-28-10 ICLR 2020 51 LAS-AT: Adversarial Training with Learnable Attack Strategy 84.98% 56.26% 56.26% × × WideResNet-34-10 arXiv, Mar 2022 52 Adversarial Weight Perturbation Helps Robust Generalization 85.36% 56.17% 56.17% × × WideResNet-34-10 NeurIPS 2020 53 A Light Recipe to Train Robust Vision Transformers 90.06% 56.14% 56.14% × ☑ XCiT-S12 arXiv, Sep 2022 54 Are Labels Required for Improving Adversarial Robustness? 86.46% 56.03% 56.03% Unknown ☑ WideResNet-28-10 NeurIPS 2019 55 Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? It uses additional 10M synthetic images in training. 84.59% 55.54% 55.54% × × ResNet-18 ICLR 2022 56 Using Pre-Training Can Improve Model Robustness and Uncertainty 87.11% 54.92% 54.92% × ☑ WideResNet-28-10 ICML 2019 57 Bag of Tricks for Adversarial Training 86.43% 54.39% 54.39% Unknown × WideResNet-34-20 ICLR 2021 58 Boosting Adversarial Training with Hypersphere Embedding 85.14% 53.74% 53.74% × × WideResNet-34-20 NeurIPS 2020 59 Learnable Boundary Guided Adversarial Training Uses ℓ ∞ = 0.031 ≈ 7.9/255 instead of 8/255 88.70% 53.57% 53.57% × × WideResNet-34-20 ICCV 2021 60 Attacks Which Do Not Kill Training Make Adversarial Learning Stronger 84.52% 53.51% 53.51% × × WideResNet-34-10 ICML 2020 61 Overfitting in adversarially robust deep learning 85.34% 53.42% 53.42% × × WideResNet-34-20 ICML 2020 62 Self-Adaptive Training: beyond Empirical Risk Minimization Uses ℓ ∞ = 0.031 ≈ 7.9/255 instead of 8/255. 83.48% 53.34% 53.34% Unknown × WideResNet-34-10 NeurIPS 2020 63 Theoretically Principled Trade-off between Robustness and Accuracy Uses ℓ ∞ = 0.031 ≈ 7.9/255 instead of 8/255. 84.92% 53.08% 53.08% Unknown × WideResNet-34-10 ICML 2019 64 Learnable Boundary Guided Adversarial Training Uses ℓ ∞ = 0.031 ≈ 7.9/255 instead of 8/255 88.22% 52.86% 52.86% × × WideResNet-34-10 ICCV 2021 65 Adversarial Robustness through Local Linearization 86.28% 52.84% 52.84% Unknown × WideResNet-40-8 NeurIPS 2019 66 Efficient and Effective Augmentation Strategy for Adversarial Training 85.71% 52.48% 52.48% × × ResNet-18 NeurIPS 2022 67 Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning Uses ensembles of 3 models. 86.04% 51.56% 51.56% Unknown × ResNet-50 CVPR 2020 68 Efficient Robust Training via Backward Smoothing 85.32% 51.12% 51.12% Unknown × WideResNet-34-10 arXiv, Oct 2020 69 Scaling Adversarial Training to Large Perturbation Bounds 80.24% 51.06% 51.06% × × ResNet-18 ECCV 2022 70 Improving Adversarial Robustness Through Progressive Hardening 86.84% 50.72% 50.72% Unknown × WideResNet-34-10 arXiv, Mar 2020 71 Robustness library 87.03% 49.25% 49.25% Unknown × ResNet-50 GitHub, Oct 2019 72 Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models 87.80% 49.12% 49.12% Unknown × WideResNet-34-10 IJCAI 2019 73 Metric Learning for Adversarial Robustness 86.21% 47.41% 47.41% Unknown × WideResNet-34-10 NeurIPS 2019 74 You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle Focuses on fast adversarial training. 87.20% 44.83% 44.83% Unknown × WideResNet-34-10 NeurIPS 2019 75 Towards Deep Learning Models Resistant to Adversarial Attacks 87.14% 44.04% 44.04% Unknown × WideResNet-34-10 ICLR 2018 76 Understanding and Improving Fast Adversarial Training Focuses on fast adversarial training. 79.84% 43.93% 43.93% Unknown × PreActResNet-18 NeurIPS 2020 77 Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness 80.89% 43.48% 43.48% Unknown × ResNet-32 ICLR 2020 78 Fast is better than free: Revisiting adversarial training Focuses on fast adversarial training. 83.34% 43.21% 43.21% Unknown × PreActResNet-18 ICLR 2020 79 Adversarial Training for Free! 86.11% 41.47% 41.47% Unknown × WideResNet-34-10 NeurIPS 2019 80 MMA Training: Direct Input Space Margin Maximization through Adversarial Training 84.36% 41.44% 41.44% Unknown × WideResNet-28-4 ICLR 2020 81 A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs Compressed model 87.32% 40.41% 40.41% × × ResNet-18 ASP-DAC 2021 82 Controlling Neural Level Sets Uses ℓ ∞ = 0.031 ≈ 7.9/255 instead of 8/255. 81.30% 40.22% 40.22% Unknown × ResNet-18 NeurIPS 2019 83 Robustness via Curvature Regularization, and Vice Versa 83.11% 38.50% 38.50% Unknown × ResNet-18 CVPR 2019 84 Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training 89.98% 36.64% 36.64% Unknown × WideResNet-28-10 NeurIPS 2019 85 Adversarial Interpolation Training: A Simple Approach for Improving Model Robustness 90.25% 36.45% 36.45% Unknown × WideResNet-28-10 OpenReview, Sep 2019 86 Adversarial Defense via Learning to Generate Diverse Attacks 78.91% 34.95% 34.95% Unknown × ResNet-20 ICCV 2019 87 Sensible adversarial learning 91.51% 34.22% 34.22% Unknown × WideResNet-34-10 OpenReview, Sep 2019 88 Towards Stable and Efficient Training of Verifiably Robust Neural Networks Verifiably robust model with 32.24% provable robust accuracy 44.73% 32.64% 32.64% Unknown × 5-layer-CNN ICLR 2020 89 Bilateral Adversarial Training: Towards Fast Training of More Robust Models Against Adversarial Attacks 92.80% 29.35% 29.35% Unknown × WideResNet-28-10 ICCV 2019 90 Enhancing Adversarial Defense by k-Winners-Take-All Uses ℓ ∞ = 0.031 ≈ 7.9/255 instead of 8/255. 7.40% robust accuracy is due to 1 restart of APGD-CE and 30 restarts of Square Attack Note: this adaptive evaluation (Section 5) reports 0.16% robust accuracy on a different model (adversarially trained ResNet-18). 79.28% 18.50% 7.40% ☑ × DenseNet-121 ICLR 2020 91 Manifold Regularization for Adversarial Robustness 90.84% 1.35% 1.35% Unknown × ResNet-18 arXiv, Mar 2020 92 Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks 89.16% 0.28% 0.28% Unknown × ResNet-110 ICCV 2019 93 Jacobian Adversarially Regularized Networks for Robustness 93.79% 0.26% 0.26% Unknown × WideResNet-34-10 ICLR 2020 94 ClusTR: Clustering Training for Robustness 91.03% 0.00% 0.00% Unknown × WideResNet-28-10 arXiv, Jun 2020 95 Standardly trained model 94.78% 0.0% 0.0% Unknown × WideResNet-28-10 N/A

LLM Generation

Query

Output

{
"2024": 69.71,
"2023": 71.07,
"2022": 65.79,
"2021": 71.28,
"2020": 65.87,
"2019": 59.53,
"2018": 44.04
}

Extract Json

I extracted the following JSON from that output:

{
"2024": 69.71,
"2023": 71.07,
"2022": 65.79,
"2021": 71.28,
"2020": 65.87,
"2019": 59.53,
"2018": 44.04
}

JSON Subset Evaluator

Testing if the previous output matches the JSON: { "2024": 69.71, "2023": 71.07, "2022": 65.79, "2021": 66.56, "2020": 65.87, "2019": 59.53, "2018": 44.04 }: False

Initial Query

LLM Generation

Query

Output

{
"2018": 44.04,
"2019": 59.53,
"2020": 65.87,
"2021": 71.07,
"2022": 65.79,
"2023": 70.69,
"2024": 69.71
}

Extract Json

I extracted the following JSON from that output:

{
"2018": 44.04,
"2019": 59.53,
"2020": 65.87,
"2021": 71.07,
"2022": 65.79,
"2023": 70.69,
"2024": 69.71
}

JSON Subset Evaluator

Testing if the previous output matches the JSON: { "2024": 69.71, "2023": 71.07, "2022": 65.79, "2021": 66.56, "2020": 65.87, "2019": 59.53, "2018": 44.04 }: False

Initial Query

LLM Generation

Query

Output

{
    "2024": 69.71,
    "2023": 71.07,
    "2022": 65.79,
    "2021": 71.28,
    "2020": 65.87,
    "2019": 59.53,
    "2018": 44.04 
}

Extract Json

I extracted the following JSON from that output:

{
    "2024": 69.71,
    "2023": 71.07,
    "2022": 65.79,
    "2021": 71.28,
    "2020": 65.87,
    "2019": 59.53,
    "2018": 44.04 
}

JSON Subset Evaluator

Testing if the previous output matches the JSON: { "2024": 69.71, "2023": 71.07, "2022": 65.79, "2021": 66.56, "2020": 65.87, "2019": 59.53, "2018": 44.04 }: False

Initial Query

LLM Generation

Query

Output

{
"2024": 69.71,
"2023": 71.07,
"2022": 65.79,
"2021": 66.56,
"2020": 65.87,
"2019": 59.53,
"2018": 44.04
}

Extract Json

I extracted the following JSON from that output:

{
"2024": 69.71,
"2023": 71.07,
"2022": 65.79,
"2021": 66.56,
"2020": 65.87,
"2019": 59.53,
"2018": 44.04
}

JSON Subset Evaluator

Testing if the previous output matches the JSON: { "2024": 69.71, "2023": 71.07, "2022": 65.79, "2021": 66.56, "2020": 65.87, "2019": 59.53, "2018": 44.04 }: True

Initial Query

LLM Generation

Query

Output

{
"2024": 69.71,
"2023": 71.07,
"2022": 65.79,
"2021": 71.28,
"2020": 65.87,
"2019": 59.53,
"2018": 44.04
}

Extract Json

I extracted the following JSON from that output:

{
"2024": 69.71,
"2023": 71.07,
"2022": 65.79,
"2021": 71.28,
"2020": 65.87,
"2019": 59.53,
"2018": 44.04
}

JSON Subset Evaluator

Testing if the previous output matches the JSON: { "2024": 69.71, "2023": 71.07, "2022": 65.79, "2021": 66.56, "2020": 65.87, "2019": 59.53, "2018": 44.04 }: False