The Deconvolutional Dimension for Discrete Hashing


The Deconvolutional Dimension for Discrete Hashing – We propose a novel method for embedding a large set of unsupervised data into a single latent variable. In this work, we first show that the unsupervised sparse estimation of features via learning over the unsupervised data is possible without requiring any supervised learning. We then show that the sparse estimation learning method is much more efficient than the sparse estimation learned through unsupervised learning. Our method, as well as some new ones, are available in our paper and we have implemented it on a single Ubuntu 15.04 operating system.

In this paper, we propose a novel approach to the supervised learning task of video classification, specifically supervised learning of the joint rank of unlabeled and unconstrained video data. Specifically, in this paper, a novel deep neural network is used to learn a weighted image classification task that optimizes an upper bound for a classifier. We show that the proposed approach is very robust to overfitting, and indeed it outperforms existing supervised learning benchmarks. Furthermore, it has the possibility to learn the joint rank of unlabeled and unconstrained labeled video data. We further show that, the proposed approach obtains competitive label quality results compared to standard unlabeled and unconstrained datasets, and that it achieves a reduced classification time.

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The Deconvolutional Dimension for Discrete Hashing

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  • Efficient Online Sufficient Statistics for Transfer in Machine Learning with Deep Learning

    Robust Multi-Labeling Convolutional Neural Networks for Driver Activity Tagged VideosIn this paper, we propose a novel approach to the supervised learning task of video classification, specifically supervised learning of the joint rank of unlabeled and unconstrained video data. Specifically, in this paper, a novel deep neural network is used to learn a weighted image classification task that optimizes an upper bound for a classifier. We show that the proposed approach is very robust to overfitting, and indeed it outperforms existing supervised learning benchmarks. Furthermore, it has the possibility to learn the joint rank of unlabeled and unconstrained labeled video data. We further show that, the proposed approach obtains competitive label quality results compared to standard unlabeled and unconstrained datasets, and that it achieves a reduced classification time.


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