What is Data Augmentation?

One of the most common challenges for companies looking to implement machine learning solutions is insufficient data. Oftentimes it is both costly and time-consuming to collect it. At the same time, the performance of machine learning and deep learning models is highly dependent on the quality, quantity and relevancy of the training data. 

This is where data augmentation comes in. 

Data augmentation can be defined as a set of techniques that artificially increase the amount of data. These techniques generate new data points from existing data and can include making small alterations to the data or using deep learning models to generate new data. 

Importance of Data Augmentation

Data augmentation techniques have been steadily growing in popularity over the past few years. There are a few reasons for this. For one, it improves the performance of machine learning models and leads to more diverse datasets. 

Many deep learning applications like object detection, image classification, image recognition, natural language understanding and semantic segmentation rely on data augmentation methods. The performance and results of deep learning models are improved by generating new and diverse training datasets. 

Data augmentation also reduces operating costs involved with data collection. For example, data labeling and collection can be both time-consuming and expensive for companies, so they rely on transforming datasets through data augmentation techniques to cut costs. 

One of the main steps of preparing a data model is to clean the data, which leads to high accuracy models. This cleaning process can reduce the representability of data, making the model unable to provide good predictions. Data augmentation techniques can be used to help the machine learning models be more robust by creating variations that the model might encounter in the real-world. 

How Does Data Augmentation Work? 

Data augmentation is often used for image classification and segmentation. It is common to make alterations on visual data, and generative adversarial networks (GANs) are used to create synthetic data. Some of the classic image processing activities for data augmentation include padding, random rotation, vertical and horizontal flipping, re-scaling, translation, cropping, zooming, changing contrast and more. 

There are a few advanced models for data augmentation: 

• Generative Adversarial Networks (GANs): GANs help learn patterns from input datasets and automatically create new examples for the training data. 
• Neural Style Transfer: These models blend content image and style image, as well as separate style from content.
  
• Reinforcement Learning: These models train agents to accomplish goals and make decisions in a virtual environment. 

Another major application for data augmentation is natural language processing (NLP). Because language is so complex, it can be extremely challenging to augment text data. 

There are a few main methods for NLP data augmentation, including easy data augmentation (EDA) operations like synonym replacement, word insertion and word swap. Another common method is back translation, which involves re-translating text from the target language back to the original language. 

Benefits and Limitations of Data Augmentation

It’s important to note that there are both benefits and limitations of data augmentation. 

When it comes to benefits, data augmentation can improve model prediction accuracy by adding more training data, preventing data scarcity, reducing data overfitting, increasing generalization, and resolving class imbalance issues in classification. 

Data augmentation also reduces the costs associated with collecting and labeling data, enables rare event prediction, and strengthens data privacy. 

At the same time, the limitations of data augmentation include a high cost of quality assurance of the augmented datasets. It also involves heavy research and development to build synthetic data with advanced applications. 

If you are using data augmentation techniques like GANs, verification can prove difficult. It is also challenging to address the inherent bias of original data if it persists in augmented data. 

Data Augmentation Use Cases

Data augmentation is one of the most popular methods for artificially increasing amounts of data for training AI models, and it is used across a wide range of domains and industries. 

Two of the most prominent industries leveraging the power of data augmentation are autonomous vehicles and healthcare: 

• Autonomous Vehicles: Data augmentation is important for the development of autonomous vehicles. Simulation environments built with reinforcement learning mechanisms help train and test AI systems with data scarcity. The simulation environment can be modeled based on specific requirements to generate real-world examples.

• Healthcare: The healthcare industry uses data augmentation as well. Oftentimes, a patient’s data cannot be used to train a model, meaning a lot of the data gets filtered from being trained. In other instances, there is not enough data around a specific disease, so the data can be augmented with variants of the existing one. 

How to Augment Data

If you are looking to augment data, you should start by identifying gaps in your data. This could involve looking for missing demographic information, for example. All activities should also support your company’s mission, so it's important to prioritize gaps based on how the information would advance the mission. 

The next step is to identify where you will get the missing data, such as through a third-party data set. When evaluating the data, you should look at cost, completeness, and the level of complexity and effort needed for integration. 

Data augmentation can take time, so it’s important to plan out the time and resources. A lot of third-party data sources require investments. It’s also critical to plan how the data will be collected and acquired, and the ROI of the data should be evaluated. 

The last step is to determine where the data will be stored, which could involve adding it to a field in your AMS or some other system. 

Of course, this is just a basic outline for the process of data augmentation. The actual process will include a lot more, which is why it is crucial to have a well-equipped team of data scientists and other experts. But by planning out and executing a data augmentation process, you can ensure your organization has the best possible data for accurate predictions.