What are the key techniques for image preprocessing in deep learning?

What are the key techniques for image preprocessing in deep learning?

Key techniques include normalization, resizing, data augmentation, and noise reduction.

Can you explain data augmentation and why it's important?

Data augmentation involves creating modified versions of images to increase dataset diversity, helping to prevent overfitting.

What are some common methods of normalization?

Common methods include min-max scaling and z-score normalization, which help standardize pixel values.

How does resizing affect model performance?

Resizing can impact the model's ability to learn features; too much distortion can lead to loss of important details.