publication . Part of book or chapter of book . Conference object . 2016

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

Pittaras, Nikiforos; Markatopoulou, Foteini; Mezaris, Vasileios; Patras, Ioannis;
Open Access
  • Published: 31 Dec 2016
  • Publisher: Springer International Publishing
Abstract
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...
Subjects
free text keywords: Annotation, Deep learning, Video annotation, Artificial intelligence, business.industry, business, Machine learning, computer.software_genre, computer, Fine-tuning, Pattern recognition, TRECVID, Computer science, Convolutional neural network
Funded by
EC| InVID
Project
InVID
In Video Veritas – Verification of Social Media Video Content for the News Industry
  • Funder: European Commission (EC)
  • Project Code: 687786
  • Funding stream: H2020 | IA
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publication . Part of book or chapter of book . Conference object . 2016

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

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