Conference paper Open Access

Comparison of Fine-tuning and Extension Strategies for Deep Convolutional Neural Networks

Pittaras, Nikiforos; Markatopoulou, Foteini; Mezaris, Vasileios; Patras, Ioannis


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{
  "description": "<p>In this study we compare three different fine-tuning strategies in order to investigate the best way to transfer the parameters of popular deep convolutional neural networks that were trained for a visual annotation task on one dataset, to a new, considerably different dataset. We focus on the concept-based image/video annotation problem and use ImageNet as the source dataset, while the TRECVID SIN 2013 and PASCAL VOC-2012 classification datasets are used as the target datasets. A large set of experiments examines the effectiveness of three fine-tuning strategies on each of three different pre-trained DCNNs and each target dataset. The reported results give rise to guidelines for effectively fine-tuning a DCNN for concept-based visual annotation.</p>", 
  "license": "http://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece", 
      "@type": "Person", 
      "name": "Pittaras, Nikiforos"
    }, 
    {
      "affiliation": "Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece", 
      "@type": "Person", 
      "name": "Markatopoulou, Foteini"
    }, 
    {
      "affiliation": "Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece", 
      "@type": "Person", 
      "name": "Mezaris, Vasileios"
    }, 
    {
      "affiliation": "Queen Mary University of London, Mile end Campus, UK", 
      "@type": "Person", 
      "name": "Patras, Ioannis"
    }
  ], 
  "headline": "Comparison of Fine-tuning and Extension Strategies for Deep Convolutional Neural Networks", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2016-12-31", 
  "url": "https://zenodo.org/record/240853", 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.1007/978-3-319-51811-4_9", 
  "@id": "https://doi.org/10.1007/978-3-319-51811-4_9", 
  "@type": "ScholarlyArticle", 
  "name": "Comparison of Fine-tuning and Extension Strategies for Deep Convolutional Neural Networks"
}
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