A survey on Image Data Augmentation for Deep Learning | Journal of Big Data | Full Text

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. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation 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 image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of aug...

3 mentions: @CShorten30@ml_review@ErmiaBivatan
Date: 2019/07/09 05:15

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@CShorten30 Really happy to share my first publication! A Survey on Image Data Augmentation for Deep Learning! https://t.co/9hw1v0GdVs #100DaysOfMLCode
@ml_review A Survey on Image Data Augmentation for Deep Learning [48pp] By @CShorten30 Geometric transform, color space augment, kernel flters, mixing images, random erasing, feature space augment, adversarial training, GANs, neural style transfer & meta-learning https://t.co/6DKAcbPASK https://t.co/LWeT4okVSY
@ErmiaBivatan A Survey on Image Data Augmentation for Deep Learning By @CShorten30 Geometric transform, color space augment, kernel flters, mixing images, random erasing, feature space augment, adversarial training. https://t.co/oHWqhE6GfO https://t.co/Np5ZU98lt9 https://t.co/pOtybfpqBY

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