What are some advanced data augmentation techniques for machine learning?

What are some advanced data augmentation techniques for machine learning?

Advanced data augmentation techniques include methods like Mixup, Cutout, and GAN-based augmentations. Would you like to know more about any specific technique?

Can you explain Mixup in detail?

Mixup is a technique where two images are blended together to create a new training sample. It helps improve model robustness by encouraging it to learn from a combination of classes.

What about Cutout? How does it work?

Cutout involves randomly masking out square regions of an image during training. This forces the model to focus on the remaining parts, improving its ability to generalize.

Are there any GAN-based techniques you recommend?

Yes, GANs can generate new training samples by learning the distribution of the training data. Techniques like StyleGAN can create high-quality augmentations.