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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.
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Speed
40.7 days to first decision for reviewed manuscripts only
40.3 days to first decision for all manuscripts
108 days from submission to acceptance
18 days from acceptance to publication
Citation Impact
4.117 - Source Normalized Impact per Paper (SNIP)
1.124 - SCImago Journal Rank (SJR)
8.79 - CiteScore
Usage
647,436 downloads
549 Altmetric mentions
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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