- @shortenSurveyImageData2019 - [[Data Augmentation with Curriculum Learning]] ## Methods - [[Geometric Transformations]] - [[Flipping]] - [[Color Space Transform.md|Color Space Transform]] - [[Cropping]] - [[Noise Injection]] - [[Color Space Transformations]] - [[Kernel Filters]] - [[Feature Space Augmentation]] - [[SMOTE]] - [[GAN‐based Data Augmentation]] - [[Meta Learning Data Augmentations]] - [[Neural Augmentation]] - [[Smart Augmentation]] - [[AutoAugment]] - [[Augmented Random Search]] - [[Test-time Augmentation]] - [[SamplePairing.md|SamplePairing]] - [[Data Augmentation with Curriculum Learning]] - [[Alleviating Class Imbalance with Data Augmentation]] ## Discussion - It is easy to explain the benefit of horizontal [[Flipping]] or random [[Cropping]] - However, it is not clear why mixing pixels or entire images together such as in PatchShuffle [[Regularization]] or SamplePairing is so effective. - dditionally, it is difficult to interpret the representations learned by neural networks for GAN-based augmentation, variational auto-encoders, and meta-learning. - An interesting characteristic of these augmentation methods is their ability to be combined together. - The GAN framework possesses an intrinsic property of recursion which is very interesting - Samples taken from GANs can be augmented with traditional augmentations such as lighting filters, or even used in neural network augmentation strategies such as [[Smart Augmentation]] to create even more samples. These samples can be fed into further GANs and dramatically increase the size of the original dataset. - An interesting question for practical Data Augmentation is how to determine postaugmented dataset size. - no consensus about the best strategy for combining data warping and oversampling techniques - One important consideration is the intrinsic bias in the initial, limited dataset - There are no existing augmentation techniques that can correct a dataset that has very poor diversity with respect to the testing data