Abstract
Learnable data augmentation is a technique where a neural netowrk learns to create modified data samples that improve the training outcome from a second, parallel neural network. This is a relatively new approach to dataset augmentation that has inspired many variations in the last few years. In this article the most signficiant of these advanced data augmentation strategies are summarised and discussed.
Original language | English |
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Pages | 55-63 |
Number of pages | 9 |
Volume | 9 |
No. | 3 |
Specialist publication | IEEE Consumer Electronics Magazine |
DOIs | |
Publication status | Published - May 2020 |