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    Inner Ensemble Networks: Average Ensemble as an Effective Regularizer

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    Type
    Preprint
    Authors
    Mohamed, Abduallah
    Sadiq, Muhammed Mohaimin
    AlBadawy, Ehab
    Elhoseiny, Mohamed cc
    Claudel, Christian
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-06-15
    Permanent link to this record
    http://hdl.handle.net/10754/666017
    
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    Abstract
    We introduce Inner Ensemble Networks (IENs) which reduce the variance within the neural network itself without an increase in the model complexity. IENs utilize ensemble parameters during the training phase to reduce the network variance. While in the testing phase, these parameters are removed without a change in the enhanced performance. IENs reduce the variance of an ordinary deep model by a factor of $1/m^{L-1}$, where $m$ is the number of inner ensembles and $L$ is the depth of the model. Also, we show empirically and theoretically that IENs lead to a greater variance reduction in comparison with other similar approaches such as dropout and maxout. Our results show a decrease of error rates between 1.7\% and 17.3\% in comparison with an ordinary deep model. We also show that IEN was preferred by Neural Architecture Search (NAS) methods over prior approaches. Code is available at https://github.com/abduallahmohamed/inner_ensemble_nets.
    Publisher
    arXiv
    arXiv
    2006.08305
    Additional Links
    https://arxiv.org/pdf/2006.08305
    Relations
    Is Supplemented By:
    • [Software]
      Title: abduallahmohamed/inner_ensemble_nets: Code for "Inner Ensemble Nets". Publication Date: 2020-06-12. github: abduallahmohamed/inner_ensemble_nets Handle: 10754/667970
    Collections
    Preprints; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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