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A Survey on Deep Learning in Image Polarity Detection: Balancing Generalization Performances and Computational Costs

1
Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, DITEN, University of Genoa, 16137 Genoa, Italy
2
School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798, Singapore
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(7), 783; https://doi.org/10.3390/electronics8070783
Received: 19 June 2019 / Revised: 8 July 2019 / Accepted: 9 July 2019 / Published: 12 July 2019
(This article belongs to the Section Artificial Intelligence)
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Abstract

Deep convolutional neural networks (CNNs) provide an effective tool to extract complex information from images. In the area of image polarity detection, CNNs are customarily utilized in combination with transfer learning techniques to tackle a major problem: the unavailability of large sets of labeled data. Thus, polarity predictors in general exploit a pre-trained CNN as the feature extractor that in turn feeds a classification unit. While the latter unit is trained from scratch, the pre-trained CNN is subject to fine-tuning. As a result, the specific CNN architecture employed as the feature extractor strongly affects the overall performance of the model. This paper analyses state-of-the-art literature on image polarity detection and identifies the most reliable CNN architectures. Moreover, the paper provides an experimental protocol that should allow assessing the role played by the baseline architecture in the polarity detection task. Performance is evaluated in terms of both generalization abilities and computational complexity. The latter attribute becomes critical as polarity predictors, in the era of social networks, might need to be updated within hours or even minutes. In this regard, the paper gives practical hints on the advantages and disadvantages of the examined architectures both in terms of generalization and computational cost. View Full-Text
Keywords: convolutional neural networks; deep learning; transfer learning; image polarity detection; computing resources convolutional neural networks; deep learning; transfer learning; image polarity detection; computing resources
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Ragusa, E.; Cambria, E.; Zunino, R.; Gastaldo, P. A Survey on Deep Learning in Image Polarity Detection: Balancing Generalization Performances and Computational Costs. Electronics 2019, 8, 783.

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