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Featured article: A survey on image data augmentation for deep learning

Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. This survey focuses on Data Augmentation, which encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The survey aims at understanding how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data.

Articles

  1. Authors: Chun-Wei Tsai, Chin-Feng Lai, Han-Chieh Chao and Athanasios V. Vasilakos

    Content type: Survey paper

Aims and scope

The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Academic researchers and practitioners will find the Journal of Big Data to be a seminal source of innovative material.

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Journal of Big Data Accepted into Scopus!

We are pleased to announce that the Journal of Big Data has been accepted into Scopus, the world's largest abstract and citation database of peer-reviewed literature. Read more about the journal's abstract and indexing on the 'About' page.

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