What are some advanced techniques for data augmentation?
Advanced techniques for data augmentation include GANs, Mixup, Cutout, and AutoAugment. Would you like details on any specific method?
Can you explain how GANs work for data augmentation?
GANs, or Generative Adversarial Networks, consist of two neural networks that compete against each other to generate new data samples that resemble the training data.
What is Mixup and how does it improve model performance?
Mixup creates new training samples by taking linear combinations of pairs of examples and their labels, which helps the model generalize better.
Are there any risks associated with using these techniques?
Yes, over-augmentation can lead to models that are too generalized or biased. It's important to balance augmentation with the original data.
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