It is a very good way to increase the number of date you have. What you’ll do depends on your data. For example, if you are training on data obtained from a sensor, you may want to add some noise to the training data so that you can increase your dataset. After all, you can expect some noise coming from the sensor later on.
Assuming that you will train it on images, here is a very good github repository that provides means to use those techniques. This python library helps you with augmenting images for your machine learning projects. It converts a set of input images into a new, much larger set of slightly altered images.
Link: https://github.com/aleju/imgaug
Features:
-
Most standard augmentation techniques available.
-
Techniques can be applied to both images and keypoints/landmarks on
images. Define your augmentation sequence once at the start of the
experiment, then apply it many times. -
Define flexible stochastic ranges for each augmentation, e.g. “rotate
each image by a value between -45 and 45 degrees” or “rotate each
image by a value sampled from the normal distribution N(0, 5.0)”. -
Easily convert all stochastic ranges to deterministic values to
augment different batches of images in the exactly identical way
(e.g. images and their heatmaps).
