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An Analysis of Convolutional Neural Network for Fashion Images Classification (Fashion-MNIST)

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246263" target="_blank" >RIV/61989100:27240/20:10246263 - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/chapter/10.1007%2F978-3-030-50097-9_10" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-50097-9_10</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-030-50097-9_10" target="_blank" >10.1007/978-3-030-50097-9_10</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    An Analysis of Convolutional Neural Network for Fashion Images Classification (Fashion-MNIST)

  • Original language description

    Recently, Convolutional Neural Networks (CNN) has been used in variety of domains, including fashion classification. Social media, e-commerce, and criminal law are extensively applicable in this field. CNNs are efficient to train and found to give the most accurate results in solving real world problems. In this paper, we use Fashion MNIST dataset for evaluating the performance of convolutional neural network based deep learning architectures. We compare most common deep learning architectures such as AlexNet, GoogleNet, VGG, ResNet, DenseNet and SqueezeNet to find the best performance. We additionally propose a simple modification to the architecture to improve and accelerate learning process. We report accuracy measurements (93.43%) and the value of loss function (0.19) using our proposed method and show its significant improvements over other architectures.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Advances in Intelligent Systems and Computing. Volume 1156

  • ISBN

    978-3-030-50096-2

  • ISSN

    2194-5357

  • e-ISSN

    2194-5365

  • Number of pages

    11

  • Pages from-to

    85-95

  • Publisher name

    Springer

  • Place of publication

    Cham

  • Event location

    Ostrava

  • Event date

    Dec 2, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000590145400010