What is Data Augmentation?

Data augmentation is the process of artificially generating new data from existing data, primarily to train machine learning (ML) models. ML models require large and varied datasets for initial training, but sourcing sufficiently diverse real-world datasets can be challenging because of data silos, regulations, and other limitations. Data augmentation artificially increases the dataset by making small changes to the original data, such as creating fully artificially generated data. Generative artificial intelligence (AI) solutions are being used for high-quality and fast data augmentation in various industries. Data augmentation should involve oversight to minimize bias skew.

ML models, including deep learning models, rely on large volumes of diverse data to develop accurate predictions in various contexts. Data augmentation helps in the creation of data variations that can help a model improve the accuracy of its predictions. For smaller initial datasets, augmented data can be vital in training.

Here are some of the benefits of data augmentation.

Enhanced model performance

Data augmentation techniques can help enrich datasets by creating many variations of existing data. This provides a larger dataset for training and enables a model to encounter more diverse features. The augmented data helps the model better generalize to unseen data and improve its overall performance in real-world environments.

Reduced data dependency

The collection and preparation of large data volumes for training can be costly and time-consuming. Data augmentation techniques increase the effectiveness of smaller datasets, reducing the dependency on existing, large datasets in training environments. You can use smaller datasets to supplement the set with synthetic data points.

Mitigate overfitting in training data

Data augmentation helps reduce the risk of overfitting when you’re training ML models. Overfitting is the undesirable ML behavior where a model can accurately provide predictions for training data, but it struggles with new data. If a model trains only with a narrow dataset, it can become overfit and can give predictions related only to that specific data type. In contrast, data augmentation provides a much larger and more comprehensive dataset for model training. It makes training sets appear unique to deep neural networks, preventing them from learning to work with only specific characteristics.

Improved data privacy

If you need to train a deep learning model on sensitive data, you can use augmentation techniques on the existing data to create synthetic data. This augmented data retains the input data's statistical properties and weights while protecting and limiting access to the original dataset.

Handling limited data in deep learning

Many deep learning scenarios have limited access to real-world data, despite needing large volumes to train machine learning models. If you have limited labeled data, regulatory restrictions, or other barriers that prevent you from accessing large volumes of content, then collecting more data might not be a feasible solution.

Data augmentation addresses this challenge, expanding the size and diversity of existing training data. You can use synthetic data generation to improve how machine learning models learn underlying patterns, all without needing to change the core data.

For deep learning, where a small volume of data can cause generalizations, augmentation facilitates much stronger machine learning model performance than working with the raw data or test data alone.

Data augmentation offers several applications in various industries, improving the performance of ML models across many sectors.

Healthcare

Data augmentation is a useful technology in medical imaging because it helps improve diagnostic models that detect, recognize, and diagnose diseases based on images. The creation of an augmented image provides more training data for models, for example, for rare diseases that lack source data variations. The production and use of synthetic patient data advances medical research while respecting all data privacy considerations.

Finance

Augmentation helps produce synthetic instances of fraud, enabling models to train to detect fraud more accurately in real-world scenarios. Larger pools of training data help in risk assessment scenarios, enhancing the potential of deep learning models to accurately assess risk and predict future trends.

Manufacturing

The manufacturing industry uses ML models to identify visual defects in products. By supplementing real-world data with augmented images, models can improve their image recognition capabilities and locate potential defects. This strategy also reduces the likelihood of shipping a damaged or defective product to factories and production lines.

Retail

Retail environments use models to identify products and assign them to categories based on visual factors. Data augmentation can produce synthetic data variations of product images, creating a training set that has more variance in terms of lighting conditions, image backgrounds, and product angles.

Autonomous systems and transport

Self-driving vehicles rely on machine learning and computer vision to understand their surroundings in real-time and react to them. These systems use large volumes of image data to train, introducing variables like different weather and lighting to improve model performance across different settings.

Image data augmentation strengthens this training process, producing augmented images from each original image. By using geometric transformations, such as flipping an image horizontally, random cropping, brightness adjustment, and contrast adjustment, you can create realistic modified versions of existing images.

By integrating structured image augmentation techniques into your training workflows for autonomous vehicles, you have a much larger pool of data to draw upon. This leads to better model accuracy and boosts overall model performance, which is vital in this high-risk field.

Data augmentation transforms, edits, or modifies existing data to create variations. The following is a brief overview of the process.

Dataset exploration

The first stage of data augmentation is to analyze an existing dataset and understand its characteristics. Features such as the size of input images, the distribution of the data, or the text structure all give further context for augmentation.

You can select different data augmentation techniques based on the underlying data type and the desired results. For example, augmenting a dataset with many images includes adding noise to them, scaling, or cropping them. Alternatively, augmenting a text dataset for natural language processing (NLP) replaces synonyms or paraphrase excerpts.

Augmentation of existing data

After you’ve selected the data augmentation technique that works best for your desired goal, you begin applying different transformations. Your selected augmentation method transforms data points or image samples in the dataset, creating a range of new augmented samples.

During the augmentation process, you maintain the same labeling rules for data consistency, ensuring that the synthetic data includes the same labels corresponding to the source data.

Typically, you look through the synthetic images to determine whether the transformation succeeded. This additional human-in-the-loop step helps maintain higher data quality.

Integrate data forms

Next, you combine the new, augmented data with the original data to produce a larger training dataset for the ML model. When you’re training the model, you use this composite dataset of both kinds of data.

Note that synthetic data augmentation creates new data points with the same bias as the original input data. To prevent biases from transferring into your new data, address any bias in the source data before starting the data augmentation process.

Data augmentation techniques vary across different data types and distinct business contexts.

Computer vision

Data augmentation is a central technique in computer vision tasks. It helps create diverse data representations and address class imbalances in a training dataset.

The first usage of augmentation in computer vision is through position augmentation. This strategy crops, flips, or rotates an input image to create augmented images. Cropping involves either resizing the image or cropping a specific portion of the original image to generate a new one. Rotation, flip, and resizing transformations all alter the original randomly with a given probability of providing new images.

Another usage of augmentation in computer vision is in color augmentation. This strategy adjusts the elementary factors of a training image, such as its brightness, contrast degree, or saturation. These common image transformations change the hue, dark and light balance, and separation between an image's darkest and lightest areas to create augmented images.

Advanced image augmentation for deep learning

For input to deep convolutional neural networks, advanced image augmentation can go beyond cropping and color adjustments. Image data augmentation techniques often manipulate the spatial properties in images, rather than just shifting pixel colors or tone. For example, random erasing could remove regions of an original image, and then you would make the model infer based on the fragmented visual rather than an entire image. This produces more robust augmented images, allowing you to generate synthetic data that meets complex image-based AI training workflows.

Audio data augmentation

Audio files, such as speech recordings, are also a common use case for data augmentation. Audio transformations typically include injecting random or Gaussian noise into some audio, fast-forwarding parts, changing the speed of parts by a fixed rate, or altering the pitch.

Text data augmentation

Text augmentation is a common data augmentation technique for NLP and other text-related applications of ML. Transformations of text data include shuffling sentences, changing the positions of words, replacing words with close synonyms, inserting random words, and deleting random words.

Neural style transfer

Neural style transfer is an advanced form of data augmentation that deconstructs images into separate fields. It uses a series of convolutional layers that separate the style and content of an image, producing many images from a single one.

Adversarial training

Changes on the pixel level create a challenge for an ML model. Adversaries can include these changes to try and trick the model. Some augmentation samples include a layer of imperceptible noise over an image to test the model’s ability to classify the image underneath. This approach is a proactive method of augmenting data that focuses on adversarial training.

Generative AI is useful in data augmentation because it facilitates the production of synthetic data. It helps increase data diversity, streamline the creation of realistic data, and preserve data privacy.

Generative adversarial networks

Generative adversarial networks (GANs) are two central neural networks that work in opposition. The generator produces samples of synthetic data, and then the discriminator distinguishes between the real data and the synthetic samples.

Over time, GANs continually improve the generator's output by focusing on deceiving the discriminator. Data that can fool the discriminator counts as high-quality synthetic data, providing data augmentation with highly reliable samples that closely mimic the original data distribution.

Variational autoencoders

Variational autoencoders (VAEs) are a type of neural network that helps increase the sample size of core data and reduces the need for time-consuming data collection. VAEs have a single connected network with two components: an encoder and a decoder. For example, in an image dataset, the encoder takes sample images and translates them into an intermediate representation. The decoder takes the representation and recreates similar images based on its understanding of the initial samples. VAEs are useful because they can create data highly similar to sample data, helping add variety while maintaining the original data distribution.

Diffusion models

Diffusion models allow you to input images and produce augmented data by adding and subtracting noise. For example, a diffusion model adds noise to the image at a random level during diffusion and outputs different images based on this information after denoising. The results output both similar and strikingly different images, due to the randomization of the noise level during image insertion.

Generative AI services on Amazon Web Services (AWS) is a set of technologies that organizations of all sizes can use to build and scale generative AI applications with customized data for custom use cases. You can innovate faster with new capabilities, a choice of industry-leading foundation models (FMs), and the most cost-effective infrastructure. The following are two examples of generative AI services on AWS.

Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies. You can securely integrate and deploy generative AI capabilities for data augmentation without managing infrastructure.

Amazon Rekognition is a fully managed AI service that offers pre-trained and customizable computer vision capabilities to extract information and insights from your images and videos. The development of a custom model to analyze images is a significant undertaking that requires time, expertise, and resources. It often requires thousands or tens of thousands of hand-labeled images to provide the model with enough data to make decisions accurately.

With Amazon Rekognition Custom Labels, various data augmentations are performed for model training, including random cropping of the image, color jittering, and random Gaussian noise. Instead of thousands of images, you need to upload only a small set of training images (typically a few hundred or fewer) specific to your use case to the easy-to-use console.

Get started with data augmentation on AWS by creating a free account today.