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  "title_listed": "A Discussion of 'Adversarial Examples'",
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  "title": "A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features'",
  "body_markdown": "Adversarial Example Researchers Need to Expand What is Meant by\n                            “Robustness”\nAuthors\nAffiliations\nJustin Gilmer\nGoogle Brain Team\nDan Hendrycks\nUC Berkeley\nJustin and Dan discuss “non-robust features” as a special case\n                    of models being non-robust because they latch on to superficial correlations,\n                    a view often found in the distributional robustness literature.\n                    As an example, they discuss recent analysis of how neural networks behave in frequency space.\n                    They emphasize we should think about a broader notion of robustness.\nRead Full Article\nComment from original authors:\nThe demonstration of models that learn from only high-frequency components of the data is\n                        an interesting finding that provides us with another way our models can learn from data that\n                        appears “meaningless” to humans.\n                        The authors fully agree that studying a wider notion of robustness will become increasingly\n                        important in ML, and will help us get a better grasp of features we actually want our models\n                        to rely on.",
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