Performance Improvement of Video Classification using Generated Labeled Data

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.