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Data augmentation is the process of artificially generating new data from existing data, primarily to train new 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. Generative artificial intelligence (AI) solutions are now being used for high-quality and fast data augmentation in various industries.
Why is data augmentation important?
Deep learning models rely on large volumes of diverse data to develop accurate predictions in various contexts. Data augmentation supplements the creation of data variations that can help a model improve the accuracy of its predictions. Augmented data is vital in training.
Here are some of the benefits of data augmentation.
Enhanced model performance
Data augmentation techniques 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, vastly reducing the dependency on 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 prevent 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 to only 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.
What are the use cases of data augmentation?
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, especially 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 project 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.
How does data augmentation work?
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 like 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 paraphrases excerpts.
Augmentation of existing data
After you’ve selected the data augmentation technique that work best for your desired goal, you begin applying different transformations. Data points or image samples in the dataset transform by using your selected augmentation method, providing 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-led 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.
It’s important to note that new data points that are created by synthetic data augmentation carry 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.
What are some data augmentation techniques?
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 tackle 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 either resizes the image or crops a small part of the original image to create a new one. Rotation, flip, and resizing transformation 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.
Audio data augmentation
Audio files, such as speech recordings, are also a common field where you can use 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 vital data augmentation technique for NLP and other text-related sectors 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 smaller parts. It uses a series of convolutional layers that separate the style and context of an image, producing many images from a single one.
Adversarial training
Changes on the pixel level create a challenge for an ML model. Some samples include a layer of imperceptible noise over an image to test the model’s ability to perceive the image underneath. This strategy is a preventative form of data augmentation focusing on potential unauthorized access in the real world.
What is the role of generative AI in data augmentation?
Generative AI is essential 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 (GAN) are a framework of two central neural networks that work in opposition. The generator produces samples of synthetic data, 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 (VAE) are a type of neural network that help to increase the sample size of core data and reduce the need for time-consuming data collection. VAEs have two connected networks: a decoder and an encoder. 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.
