What are the common techniques used for data augmentation?

What are the common techniques used for data augmentation?

Some common techniques used for data augmentation include:

1. Image flipping: Flipping images horizontally or vertically to create new variations of the same image.

2. Rotation: Rotating images by a certain angle to generate different perspectives.

3. Scaling: Resizing images to different scales, either larger or smaller, to introduce variations in size.

4. Translation: Shifting images horizontally or vertically to create new positions within the image.

5. Cropping: Removing parts of an image to focus on specific regions or objects.

6. Noise injection: Adding random noise to images to simulate real-world variations.

7. Color jittering: Modifying the color properties of images, such as brightness, contrast, saturation, or hue.

8. Elastic deformation: Distorting images using elastic transformations to introduce deformations.

9. Gaussian blur: Applying a blur effect to images to reduce noise or enhance certain features.

10. Data mixing: Combining multiple images or data samples to create new training examples.

These techniques help increase the diversity and quantity of training data, which can improve the performance and generalization of machine learning models.