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  • title: Take 5: Interpretable Image Classification with a Handful of Features
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            Take 5: Interpretable Image Classification with a Handful of Features
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            Take 5: Interpretable Image Classification with a Handful of Features

            Dec 2, 2022

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            Thomas Norrenbrock

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            Marco Rudolph

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            Bodo Rosenhahn

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            About

            Deep Neural Networks use thousands of mostly incomprehensible features to identify a single class, a decision no human can follow. We propose an interpretable sparse and low dimensional final decision layer in a deep neural network with measurable aspects of interpretability and demonstrate it on fine-grained image classification. We argue that a human can only understand the decision of a machine learning model, if the input features are interpretable and only very few of them are used for a si…

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            NeurIPS 2022

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