RACKNet: Robust allocation of convolutional kernels in neural networks for image classification

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Scopus citations

    Abstract

    Despite their impressive success when these hyper-parameters are suitably fine-tuned, the design of good network architectures remains an art-form rather than a science: while various search techniques, such as grid-search, have been proposed to find effective hyper-parameter configurations, often these parameters are hand-crafted (or the bounds of the search space are provided by a user). In this paper, we argue, and experimentally show, that we can minimize the need for hand-crafting, by relying on the dataset itself. In particular, we show that the dimensions, distributions, and complexities of localized features extracted from the data can inform the structure of the neural networks and help better allocate limited resources (such as kernels) to the various layers of the network. To achieve this, we first present several hypotheses that link the properties of the localized image features to the CNN and RCNN architectures and then, relying on these hypotheses, present a RACKNet framework which aims to learn multiple hyperparameters by extracting information encoded in the input datasets. Experimental evaluations of RACKNet against major benchmark datasets, such as MNIST, SVHN, CIFAR10, COIL20 and ImageNet, show that RACKNet provides significant improvements in the network design and robustness to change in the network.

    Original languageEnglish (US)
    Title of host publicationICMR 2019 - Proceedings of the 2019 ACM International Conference on Multimedia Retrieval
    PublisherAssociation for Computing Machinery, Inc
    Pages315-323
    Number of pages9
    ISBN (Electronic)9781450367653
    DOIs
    StatePublished - Jun 5 2019
    Event2019 ACM International Conference on Multimedia Retrieval, ICMR 2019 - Ottawa, Canada
    Duration: Jun 10 2019Jun 13 2019

    Publication series

    NameICMR 2019 - Proceedings of the 2019 ACM International Conference on Multimedia Retrieval

    Conference

    Conference2019 ACM International Conference on Multimedia Retrieval, ICMR 2019
    CountryCanada
    CityOttawa
    Period6/10/196/13/19

    Keywords

    • Convolutional neural networks
    • Deep learning
    • Hyper-parameter optimization
    • Meta-learning
    • Recurrent neural networks

    ASJC Scopus subject areas

    • Software
    • Computer Graphics and Computer-Aided Design
    • Computer Vision and Pattern Recognition

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  • Cite this

    Garg, Y., & Candan, K. S. (2019). RACKNet: Robust allocation of convolutional kernels in neural networks for image classification. In ICMR 2019 - Proceedings of the 2019 ACM International Conference on Multimedia Retrieval (pp. 315-323). (ICMR 2019 - Proceedings of the 2019 ACM International Conference on Multimedia Retrieval). Association for Computing Machinery, Inc. https://doi.org/10.1145/3323873.3325057