In the digital landscape of machine learning, the quality and quantity of data play a significant role in determining the performance of models. That’s where data scientists and machine learning engineers face a tremendous challenge. They often come across scenarios where the data for their project isn’t abundant to train a machine learning model. That results in mediocre performance.
These circumstances are mainly true, especially when dealing with highly complicated deep-learning models requiring a large volume of data for optimal performance. This blog will cover the basics of data augmentation, its importance, types, and popular data augmentation techniques.
Let’s dive in.
What Is Data Augmentation?
Data augmentation is a technique commonly used in machine learning and deep learning to artificially increase the size of a training dataset by creating new variations of existing data points. The purpose of data augmentation is to improve the generalization and performance of machine learning models by exposing them to a more diverse set of examples. This is particularly useful when the original dataset is limited in size or lacks diversity.
Data augmentation involves applying various transformations to the existing data points to create new instances that are still representative of the underlying patterns in the data. These transformations typically preserve the essential features of the data while introducing variations that help the model become more robust to different conditions and variations in the real world.
Why Is Data Augmentation Important?
Data augmentation is highly important for many AI applications because training data accuracy increases with volume. As a matter of fact, studies have shown that basic data augmentation can majorly improve accuracy on image tasks such as classification and segmentation.
Furthermore, deep learning and neural networks also need a large volume of data in order to make data augmentation techniques even more effective.
This major issue is that most companies don’t have sufficient data mandatory to train their AI models. That’s where data augmentation comes in. Even if you begin with a minute of data, data augmentation can help you finish up massive amounts of data to produce insights, forecasts, and suggestions which weren’t previously possible due to a lack of relevant data. However, leveraging small volumes of data may result in overfitting; on the other hand, using large volumes helps to overcome such circumstances.
Remember, data augmentation is all about having the right and relevant data as opposed to having large volumes of data. For instance, employing an algorithm designed to forecast stock market trends to predict a baseball game’s results might lead to inconclusive results.
How you will gather and clean your augmented data before employing it in your AI model is another crucial factor to take into consideration. It’s quite simple and straightforward as you need to integrate the new data with the current data only if it’s in the same format as your previously created data. However, it gets a little more complex as you will need to be consistent in data formatting if you are generating new data or leveraging a new data source.
Types Of Data Augmentation
Real-Data Augmentation
Real-data augmentation is when you add real, actual, or additional data to a dataset. Whether it’s text files with additional information (such as labels for labeled photos), images of objects similar to the original images, or videos of the original dataset.
For example: Incorporating a few additional features into an image file can significantly help a machine learning model to identify the target object more accurately. Moreover, in order to give more information about what each image looks like and stands for prior to starting training on those images, we have the option to, for instance, add more metadata about each image, such as its name and description.
When classifying new images into one of our predetermined categories, like “bat” or “ball,” the model may be more likely to accurately identify the objects in an image and deliver a better overall result.
Real-data augmentation can also be defined as the combination of data from different sources or merely from different data files. For example, consider a situation where you are developing a model to score incoming leads. You have the option to export data directly from your HubSpot account. However, if you have Salesforce, you could combine that data to create a larger training data file, which could help you build a more accurate predictive model.
Synthetic Data Augmentation
In addition to incorporating additional data, you also have the option to add synthetic data or fake data that may resemble real data. Synthetic data augmentation is highly beneficial for challenging tasks like neural style transfer. However, it’s only helpful if you apply it to any design, whether you’re utilizing CNNs, GANs, or any other deep neural network architecture.
For example, if we want to identify Persian cats in a dataset of cat images accurately, we could simply add fake images of Persian cats without taking photographs of Persian cats. Thus, synthetic data augmentation helps collect data and increase the model’s accuracy, especially when collecting data is challenging, time-intensive, and costly.
In this example, we are synthetically increasing the dataset. Suppose 5 of the 500 cat breed images in our dataset are of Persian cats. Now instead of adding more images of real Persian cats from real cats, let’s create a synthetic image of a Persian cat by replicating an existing image and making slight alterations to it in order to resemble the original Persian cat.
Synthetic data augmentation empowers us to simply convert, suppose, five real images of Persian cats into fifty (or even more) fake ones by applying various distortions. In this manner, the model can adequately categorize personal cats with a small number of real images. These image augmentation techniques can alter the original image in various ways, including modifying the pixel colors or applying a different geometric transformation, such as a vertical flip. The model can become invariant to the rotation of the object by leveraging simple techniques like rotations. Creating robust and reliable image models requires the use of invariance.
The primary objective of synthetic augmentation is to develop a larger training set with augmented images. That improves the accuracy of computer vision tasks such as image identification, object recognition, and classification issues. Some libraries such as Keras’ ImageDataGenerator can help with image augmentation.
However, synthetic data augmentation also helps in improving natural language processing tasks. That includes creating text using language models to create more accurate language models for particular use cases subsequently.
Popular Data Augmentation Techniques
Image Data Augmentation
Image data augmentation is a common practice in incorporating additional images in computer vision tasks. There are various techniques to apply to images, including random cropping, scaling, rotation, color jittering, and more. That significantly increases the size and diversity of the dataset. Other techniques include adding noise, image occlusion, and blurring, which can imitate variations in the real world and strengthen model robustness.
Text Data Augmentation
Data augmentation techniques that modify textual data also benefit natural processing languages (NLPs) issues. Introducing variations in the text through techniques such as deletion, insertion, word substitution, and synonym replacement allows the model to become more robust to different writing styles, noise, and vocabularies. Back-translation can also produce new training examples, which involve translating a statement into another language and then back into the original language.
Time Series Data Augmentation
Techniques like scaling, shifting, jittering, and adding noise can also enhance time series data. That can be frequently seen in the sensor readings, financial sector, and weather forecasting. By simulating various scenarios, time series data augmentation can enhance model generalization and distinct patterns.
Audio Data Augmentation
Audio data augmentation techniques are used to alter audio signals in order to enhance dataset diversity. These techniques include noise injection, time stretching, pitch shifting, and background noise amplification. By leveraging enhanced audio data, models can seamlessly manage accents and variations in environmental sound classification.
Conclusion
Data augmentation will play a crucial role as machine learning and deep learning continue to sustain and thrive in the digital age. Data augmentation is a dynamic technique that enables data scientists and engineers to overcome difficult data limitations, enhance model performance, and promote ongoing advancements in deep learning and machine learning.
The data augmentation adoption and implementation across the globe empowers the efficacy and efficiency of machine learning and deep learning models across various industries and domains. Data augmentation allows us to leverage synthetic intelligence that paves the way for innovation and technology.
