What are the latest trends in data augmentation techniques?
The latest trends include generative adversarial networks (GANs), mixup techniques, and automated data augmentation.
How do GANs improve data augmentation?
GANs generate new data samples that resemble the training data, enhancing diversity and improving model robustness.
Can you explain mixup techniques?
Mixup involves creating new training samples by combining two existing samples, which helps in regularizing the model.
What are the benefits of automated data augmentation?
Automated data augmentation saves time, reduces manual effort, and can discover new augmentation strategies that improve performance.
Are there any challenges with these techniques?
Yes, challenges include computational costs, potential overfitting, and the need for careful tuning of augmentation parameters.
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