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• W2751127531 abstract "We propose introspective convolutional networks (ICN) that emphasize the importance of having convolutional neural networks empowered with generative capabilities. We employ a reclassification-by-synthesis algorithm to perform training using a formulation stemmed from the Bayes theory. Our ICN tries to iteratively: (1) synthesize pseudo-negative samples; and (2) enhance itself by improving the classification. The single CNN classifier learned is at the same time generative --- being able to directly synthesize new samples within its own discriminative model. We conduct experiments on benchmark datasets including MNIST, CIFAR-10, and SVHN using state-of-the-art CNN architectures, and observe improved classification results." @default.
• W2751127531 created "2017-09-15" @default.
• W2751127531 creator A5001760915 @default.
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• W2751127531 date "2017-04-25" @default.
• W2751127531 modified "2023-09-26" @default.
• W2751127531 title "Introspective Classification with Convolutional Nets" @default.
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• W2751127531 doi "https://doi.org/10.48550/arxiv.1704.07816" @default.
• W2751127531 hasPublicationYear "2017" @default.
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