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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{
  "DOI": "10.1007/978-3-319-51811-4_9", 
  "author": [
    {
      "family": "Pittaras, Nikiforos"
    }, 
    {
      "family": "Markatopoulou, Foteini"
    }, 
    {
      "family": "Mezaris, Vasileios"
    }, 
    {
      "family": "Patras, Ioannis"
    }
  ], 
  "issued": {
    "date-parts": [
      [
        2016, 
        12, 
        31
      ]
    ]
  }, 
  "abstract": "<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>", 
  "title": "Comparison of Fine-tuning and Extension Strategies for Deep Convolutional Neural Networks", 
  "type": "paper-conference", 
  "id": "240853"
}
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