Machine Learning

Introspective Classification with Convolutional Nets

This topic contains 0 replies, has 1 voice, and was last updated by  arXiv 10 months, 1 week ago.


  • arXiv
    5 pts

    Introspective Classification with Convolutional Nets

    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.

    Introspective Classification with Convolutional Nets
    by Long Jin, Justin Lazarow, Zhuowen Tu
    https://arxiv.org/pdf/1704.07816v2.pdf

You must be logged in to reply to this topic.