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  • Machine Learning Seminar

    Series Name:

    Machine Learning Seminar


    Larry Carin


    Machine Learning, Bass Connections-Information, Society & Culture, Biostatistics and Bioinformatics, Computer Science, Electrical and Computer Engineering (ECE), Information Initiative at Duke (iiD), Mathematics, and Statistical Science

    Gross Hall, 330 -- Ahmadieh Family Grand Hall - Map




    Dawn, Ariel





    Machine Learning - Larry Carin

    Title: Variational Autoencoder for Deep Learning of Images, Labels and Captions
    Abstract: A novel variational autoencoder is developed to model images, as well as associated labels or captions. The Deep Generative Deconvolutional Network (DGDN) is used as a decoder of the latent image features, and a deep Convolutional Neural Network (CNN) is used as an image encoder; the CNN is used to approximate a distribution for the latent DGDN features/code. The latent code is also linked to generative models for labels (Bayesian support vector machine) or captions (recurrent neural network). When predicting a label/caption for a new image at test, averaging is performed across the distribution of latent codes; this is computationally efficient as a consequence of the learned CNN-based encoder. Since the framework is capable of modeling the image in the presence/absence of associated labels/captions, a new semi-supervised setting is manifested for CNN learning with images; the framework even allows unsupervised CNN learning, based on images alone. Several example state-of-the-art results are presented on large-scale datasets.

    Lecture/Talk and Panel/Seminar/Colloquium