Dense Associative Memory Is Robust to Adversarial Inputs
نویسندگان
چکیده
منابع مشابه
Dense Associative Memory is Robust to Adversarial Inputs
Deep neural networks (DNN) trained in a supervised way suffer from two known problems. First, the minima of the objective function used in learning correspond to data points (also known as rubbish examples or fooling images) that lack semantic similarity with the training data. Second, a clean input can be changed by a small, and often imperceptible for human vision, perturbation, so that the r...
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ژورنال
عنوان ژورنال: Neural Computation
سال: 2018
ISSN: 0899-7667,1530-888X
DOI: 10.1162/neco_a_01143