What Is Data Augmentation ?

What Is Data Augmentation?

The performance of most computer vision systems will improve as data volume increases; however, gathering more data is often complicated and expensive. Data augmentation is a common technique for improving the efficiency of computer vision systems. Data augmentation is helpful whether you are using transfer learning or training your model from scratch. Data Augmentation is a useful technique not just for machine learning, but also for any AI application like computer vision suffering from a lack of sufficient data.

Data augmentation techniques provide additional images for training by artificially creating new images from original images. Each application and domain of deep learning networks has its unique augmentation methods; for example, when training a model to recognize human speech, we add some noise to the audio signal so that the network can learn to recognize words or emotions even with seemingly opaque sounds. Other techniques, such as rotating, changing the brightness, etc. are used when it comes to detecting an object in an image or finding the exact location of several objects in an image.

It’s possible to strengthen deep neural networks by training them with more data. using image data augmentation techniques, new features are  learned from augmented images – in addition to the original images – to enable the network to comprehend a wider range of objects and let the model achieve more generalization.

One of the most well-known types of data augmentation is Image data augmentation, which involves transforming images while maintaining their class and statistical distribution. Data augmentation involves adding new, credible images to the collection to increase their size. The choice of data augmentation technique must be carefully made based on the dataset’s content and the type of the problem.