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    "Accuracy (clean data)": 81.07,
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      {
        "title": "yanemcovsky/SIAM",
        "url": "https://github.com/yanemcovsky/SIAM"
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      "title": "Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation",
      "url": "https://cknow.io/lib/4d14574cb2bcf0a6"
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      "title": "Learning to Defense by Learning to Attack",
      "url": "https://cknow.io/lib/03c1b42639911d95"
    },
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    "code_links": [],
    "date": "2020-01-01",
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    "model": "IAAT",
    "paper": {
      "title": "Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets",
      "url": "https://cknow.io/lib/b08eb77b911187ad"
    },
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        "title": "aamir-mustafa/pcl-adversarial-defense",
        "url": "https://github.com/aamir-mustafa/pcl-adversarial-defense"
      }
    ],
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      "title": "Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks",
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    "model": "PNI-W",
    "paper": {
      "title": "Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack",
      "url": "https://cknow.io/lib/62067e97d64a8e8c"
    },
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  {
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        "title": "MadryLab/mnist_challenge",
        "url": "https://github.com/MadryLab/mnist_challenge"
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      {
        "title": "locuslab/convex_adversarial",
        "url": "https://github.com/locuslab/convex_adversarial"
      },
      {
        "title": "MadryLab/cifar10_challenge",
        "url": "https://github.com/MadryLab/cifar10_challenge"
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      {
        "title": "revbucket/mister_ed",
        "url": "https://github.com/revbucket/mister_ed"
      },
      {
        "title": "tianzheng4/Distributionally-Adversarial-Attack",
        "url": "https://github.com/tianzheng4/Distributionally-Adversarial-Attack"
      },
      {
        "title": "P2333/Max-Mahalanobis-Training",
        "url": "https://github.com/P2333/Max-Mahalanobis-Training"
      },
      {
        "title": "henry8527/GCE",
        "url": "https://github.com/henry8527/GCE"
      },
      {
        "title": "Hadisalman/robust-verify-benchmark",
        "url": "https://github.com/Hadisalman/robust-verify-benchmark"
      },
      {
        "title": "locuslab/robust_overfitting",
        "url": "https://github.com/locuslab/robust_overfitting"
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      {
        "title": "bingcheng45/hnr-extension",
        "url": "https://github.com/bingcheng45/hnr-extension"
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      {
        "title": "cs-giung/course-dl-TP",
        "url": "https://github.com/cs-giung/course-dl-TP"
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      {
        "title": "amerch/CIFAR100-Training",
        "url": "https://github.com/amerch/CIFAR100-Training"
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        "title": "tianzheng4/Noisy-Training-Soft-Quantization",
        "url": "https://github.com/tianzheng4/Noisy-Training-Soft-Quantization"
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      {
        "title": "snaka0213/PyTorch-AdvAttacks",
        "url": "https://github.com/snaka0213/PyTorch-AdvAttacks"
      },
      {
        "title": "hope-yao/robust_attention_cifar",
        "url": "https://github.com/hope-yao/robust_attention_cifar"
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      {
        "title": "abahram77/mnistChallenge",
        "url": "https://github.com/abahram77/mnistChallenge"
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      {
        "title": "Harry24k/adversairal-defenses-pytorch",
        "url": "https://github.com/Harry24k/adversairal-defenses-pytorch"
      },
      {
        "title": "jxnding/dsc531_bayes",
        "url": "https://github.com/jxnding/dsc531_bayes"
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      "title": "Towards Deep Learning Models Resistant to Adversarial Attacks",
      "url": "https://cknow.io/lib/2e16f82f424bbba9"
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