For machine learning models, it is crucial to have both quantity and diversity of available data during training. Only with the help from data that meet those two conditions can you expect the machine learning model to complete given tasks.
Higher quantity of data → higher number of learnable parameters → higher chance of finishing a complex task successfully
ML models that have an obligation to perform complex tasks often have a significant neural network. Similarly to data, if there is an increase in the overall amount of neurons, there is an increase in the overall amount of learned parameters.
The overall amount of parameters that can be learned through training data augmentation varies from a few tens to a few hundred million depending on the type of deep learning model. For example, natural language processing ML models that carry out difficult tasks which include sentiment analysis, recognition of named entities, sentence segmentation, and machine translation belong to the group of models with the largest number of parameters that can be trained. To learn these parameters, a huge quantity of data is needed. It is this availability of large amounts of data that is usually a challenging obstacle.
How does data augmentation work?
The right solution to address the data problem is to use data augmentation. So how does it work? Data augmentation is simply a technique that is used to synthesize new data by applying slight transformations and modifications of already existing data. In simple terms, data augmentation is creating new data from the available data.
Data augmentation is the process of synthesizing new data from the available data
Data augmentation vs. synthetic data
Data augmentation refers to techniques for increasing the quantity of data. This is achieved by inserting slightly changed copies of data that already exists or data is synthesized from existing data.
Why is data augmentation important now?
The challenges in creating a machine learning (ML) model work on specific tasks and providing necessary data for it are tough even with transfer learning techniques.
Here is where data augmentation’s importance comes to the fore. Both the prerequisites we mentioned at the start (diversity and sheer quantity of data) can be solved with the help of various data augmentation methods. However, the importance of augmented data structure does not end there. It can even be put to use to solve imbalance classification tasks. In fact, the most popular numerical data augmentation techniques such as SMOTE or SMOTE NC are used to solve class imbalance problems.
The significant role of augmentation is clear once we see quality models for various applications with and without it. Image classification performance without it is around 57% while performance with methods such as simple image-based and GAN-based is 78% for first and 85% for the second. Text classification also jumps significantly with data augmentation – from 79% without it to 87%.
According to many case reports, data augmentation methods improve overall performance and various augmentation methods positively affect the model
The augmentation methods for simple unstructured data like images have become a great achievement. Those methods are simple changes like rotation, flipping, cropping, scaling, translation, varying brightness, or color casting.
Although quite simple and effective, these methods have their limitations. The most important is the possibility that the original image may lose its most significant features when these transformations are being applied. Therefore, more sophisticated techniques like Neural Style Transfer, GAN, and Adversarial Training, are being utilized for creating more practical transformations.
Deep Neural Network-based data augmentation methods
GAN-based augmentation – it is composed of a discriminator and a generator. The goal of the second trained neural network is, as the name suggests, to create fake images. Discriminators have a task to distinguish fake from real images.
Adversarial Training – is used to transform images so that they can be used as training data. These transform images called masks are further used to the input image so that the model can generate different augmented images.
Neural style transfer combines the structure of one image with the design of some other image in this process. That way augmented image has been created by using different images. However, the augmented image is very similar to the original (input) image, the only difference is the “new” style from the second image.
Automating the data augmentation method is an effective way to quickly develop high-performance machine learning models.