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Conference Paper Performance Improvement of Video Classification using Generated Labeled Data
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Authors
Alex Lee, Jeong-Woo Son, Sunjoong Kim
Issue Date
2020-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1417-1419
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289479
Abstract
As the development of deep learning techniques is growing, applications using deep learning have been spreading. Among various applications, images and videos related applications are the most common example of the practical deep learning application. The performances in those applications have been boosted by adopting deep learning techniques. To achieve performance, securing a large amount of data-oriented to target tasks is crucial. In this paper, we have designed the experiments to examine the effect of generated data on both where the dataset can be easily collected and hard to secure. We use state-of-the-art generative model, MCnet, to enlarge the Sexually Harmful Contents dataset and UCF-101. By training C3D with augmented data, we measure the classification performance. The generated labeled data have increased the performance by 7% on harmful content detection.
KSP Keywords
Classification Performance, Content Detection, Data-oriented, Deep learning application, Generative models, Video Classification, deep learning(DL), images and videos, labeled data, performance improvement, state-of-The-Art