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The emergence of bigger and better datasets has allowed for the expansion of deep learning systems to daily-life activities and fields including transportation (autonomous vehicles [1]), communication (bandwidth regulators), and even medicine (Enhanced diagnostic tools [4]). However, current research efforts have shown that Deep Neural Networks (DNN) are notoriously brittle to small perturbations in their input data [6], making them unreliable when faced with real-world inputs. This issue is usually associated with one of the following two defects found in DNNs:

1. Inadequate standard generalization, where trained models show high accuracy on the training set but cannot be generalized to…

Daniel Garces

Senior @ Columbia University studying Computer Engineering

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