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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.
Aims and scope
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- ISSN: 2196-1115 (electronic)