@Machine_learn
MaxUp: A Simple Way to Improve Generalization of Neural Network Training
A new approach to augmentation both images and text. The idea is to generate a set of augmented data with some random perturbations or transforms and minimize the maximum, or worst case loss over the augmented data. By doing so, the authors implicitly introduce a smoothness or robustness regularization against the random perturbations, and hence improve the generation performance. Testing MaxUp on a range of tasks, including image classification, language modeling, and adversarial certification, it is consistently outperforming the existing best baseline methods, without introducing substantial computational overhead.
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paper: https://arxiv.org/abs/2002.09024
#augmentations #SOTA #ml
@Machine_learn
MaxUp: A Simple Way to Improve Generalization of Neural Network Training
A new approach to augmentation both images and text. The idea is to generate a set of augmented data with some random perturbations or transforms and minimize the maximum, or worst case loss over the augmented data. By doing so, the authors implicitly introduce a smoothness or robustness regularization against the random perturbations, and hence improve the generation performance. Testing MaxUp on a range of tasks, including image classification, language modeling, and adversarial certification, it is consistently outperforming the existing best baseline methods, without introducing substantial computational overhead.
.
.
.
paper: https://arxiv.org/abs/2002.09024
#augmentations #SOTA #ml
