Data augmentation refers to the process of artificially increasing the amount of data by generating new points from existing datasets.
This technique is seeing increased adoption for image classification machine learning algorithms.
Image classifying ML algorithms typically require a large dataset containing various images. However, in many cases, the datasets available are very limited which can lead to “data overfitting”.
(Data overfitting is a statistical error where the function corresponds very closely to a limited dataset).
Blurring, rotating, and padding images via data augmentation enables the creation of more images within an
existing dataset.
What's Next
Data augmentation is part of the Alternative AI Training Datasets meta
trend.
Training AI models is getting more expensive. And one of the biggest reasons is that it’s difficult to obtain robust datasets.
For many startups and other smaller institutions, the cost of gathering real-life AI datasets often exceeds their entire computing budgets.
This has created the need to find alternative ways to generate the datasets required to train AI models.
For example,
synthetic data is a trending technique used to create cheaper datasets.
In fact, Gartner forecasts that by 2030, synthetic data will become the primary data source used to train
AI models.
