What are some effective dataset augmentation techniques?

What are some effective dataset augmentation techniques?

Dataset augmentation techniques are methods used to increase the diversity of your training dataset without actually collecting new data. Some effective techniques include flipping, rotating, scaling images, adding noise, and using generative models.

Can you explain how to implement image flipping?

Sure! Image flipping can be implemented using libraries like OpenCV or PIL in Python. You can use the flip function in OpenCV or the transpose method in PIL to flip images horizontally or vertically.

What about text data? Are there augmentation techniques for that?

Yes, for text data, techniques include synonym replacement, random insertion, random deletion, and back-translation. These methods help create variations of the original text while preserving its meaning.

How do these techniques impact model performance?

Properly applied augmentation techniques can significantly improve model performance by reducing overfitting and helping the model generalize better to unseen data.