Data augmentation for improving deep learning in image classification problem

1. What is data augmentation in image classification?
2. What is image augmentation in deep learning?
3. How data augmentation is helpful in deep learning explain with example?

What is data augmentation in image classification?

Image data augmentation is the process of generating new transformed versions of images from the given image dataset to increase its diversity. To a computer, images are just a 2-dimensional array of numbers. These numbers represent pixel values, which you can tweak in many ways to generate new, augmented images.

What is image augmentation in deep learning?

Image augmentation is a technique that is used to artificially expand the data-set. This is helpful when we are given a data-set with very few data samples. In case of Deep Learning, this situation is bad as the model tends to over-fit when we train it on limited number of data samples.

How data augmentation is helpful in deep learning explain with example?

Data augmentation is useful to improve the performance and outcomes of machine learning models by forming new and different examples to train datasets. If the dataset in a machine learning model is rich and sufficient, the model performs better and more accurately.