What is Data Augmentation?
Artificially creating new data from existing data is called data augmentation and is mostly used to train machine learning (ML) models. Due to data silos, limits, and other constraints, it may be difficult to gather diverse real-world datasets for machine learning model training. Small data changes provide an artificially larger dataset in data augmentation. Many industries use generative AI for quick, high-quality data augmentation.
Data augmentation creating new data from existing data is generally used to train machine learning (ML) models. Data silos, limits, and other constraints may make it hard to access diverse real-world datasets for ML model training.By making minor adjustments to the underlying data, data augmentation creates an artificially larger dataset. Many different sectors are increasingly using generative artificial intelligence (AI) technologies for quick and high-quality data augmentation.
Why is data augmentation important?
Large amounts of varied data are necessary for deep learning models to produce precise predictions in a range of situations. In order to increase a model’s prediction accuracy, data augmentation is used in addition to the production of data variants. In training, augmented data is essential.
These are a few advantages of augmenting data.
Enhanced model performance
By producing numerous variations of preexisting data, data augmentation techniques aid in the enrichment of datasets. This gives a model access to a wider range of features and a bigger dataset for training. The model performs better overall in real-world settings and is able to generalise to previously unseen data to the enhanced data.
Reduced data dependency
It can be expensive and time-consuming to gather and prepare massive amounts of data for training. By making smaller datasets more effective, data augmentation techniques significantly lessen the need for huge datasets in training settings. Smaller datasets can be used to add artificial data points to the collection.
Mitigate overfitting in training data
When you’re training machine learning models, data augmentation helps avoid over fitting. Unwanted machine learning behaviour known as over fitting occurs when a model performs well on training data but poorly on fresh data. A model may become over fit and only produce predictions pertaining to that particular data type if it is trained on a limited dataset. Data augmentation, on the other hand, offers a far more extensive and extensive dataset for model training. It prevents deep neural networks from learning to operate with only particular features by making training sets seem exclusive to them.
Improved data privacy
You can build synthetic data using augmentation techniques on the existing data if you need to train a deep learning model on sensitive data. While safeguarding and restricting access to the original, this augmented data preserves the statistical characteristics and weights of the input data.
What are the use cases of data augmentation?
Data augmentation enhances the performance of machine learning models in numerous industries and has multiple applications.
Healthcare
Because it enhances diagnostic models that identify, detect, and diagnose diseases based on images, data augmentation is a helpful technology in medical imaging. More training data is available for models when an augmented image is created, particularly for rare diseases with no variability in the source data. Synthetic patient data is created and used to further medical research while adhering to all data privacy regulations.
Finance
By creating artificial examples of fraud, augmentation helps models learn to identify fraud more precisely in real-world situations. Bigger training data sets aid in risk assessment situations, increasing deep learning models’ capacity to precisely evaluate risk and forecast future patterns.
Manufacturing
ML models are used in the manufacturing sector to detect visible flaws in goods. Models can enhance their picture recognition skills and identify possible flaws by adding augmented images to real-world data. Additionally, this approach lessens the possibility of delivering a faulty or damaged project to manufacturing facilities and production lines.
Retail
Models are used in retail settings to identify products and classify them according to visual criteria. A training set with greater variation in lighting conditions, image backdrops, and product angles can be created using data augmentation, which can generate synthetic data variants of product photographs.
How does data augmentation work?
To produce variations, data augmentation changes, adjusts, or transforms already-existing data. Here is a quick rundown of the procedure.
Dataset exploration
Analysing an existing dataset and comprehending its features is the first step in the data augmentation process. Additional context for augmentation is provided by features such as the text structure, data distribution, and input image size.
Alternatively, natural language processing (NLP) can be used to enhance a text dataset by substituting synonyms or paraphrasing passages.
Augmentation of existing data
Following the selection of the data augmentation strategy that best suits your objective, you start implementing various transformations. Using your chosen augmentation technique, data points or picture samples in the dataset change, producing a variety of new augmented samples. For data consistency, you stick to the same labelling guidelines throughout the augmentation process, making sure that the synthetic data has the same labels as the original data. Usually, you examine the artificial photos to see if the alteration was successful. Higher data quality is maintained by this extra human-led stage.
Integrate data forms
The new, enhanced data is then combined with the original data to create a bigger training dataset for the machine learning model. This composite dataset comprising both types of data is used to train the model. It’s crucial to remember that newly generated data points from synthetic data augmentation are subject to the same bias as the original input data. Before beginning the data augmentation process, correct any bias in the source data to stop biases from migrating into your new data.
What are some data augmentation techniques?
Different business contexts and data kinds require different approaches to data augmentation.
Computer vision
One of the key strategies for computer vision tasks is data augmentation. It addresses class imbalances in a training dataset and aids in the creation of varied data representations.
Position augmentation is the initial application of augmentation in computer vision. To produce augmented images, this technique rotates, flips, or cuts an input image. Cropping creates a new image by either resizing the original or cropping a small portion of it. The original is randomly altered by rotation, flipping, and resizing transformations, each of which has a certain likelihood of producing new images.
Colour augmentation is another application of augmentation in computer vision. This method modifies a training image’s basic characteristics, like its saturation, contrast level, and brightness. In order to produce enhanced images, these popular image transformations alter the colour, light and dark balance, and contrast between the darkest and lightest regions of an image.
Audio data augmentation
Data augmentation is also frequently used in audio files, such as voice recordings. Adding random or Gaussian noise to some sounds, fast-forwarding certain parts, changing the speed of certain parts by a set rate, or adjusting the pitch are examples of common audio transformations.
Text data augmentation
For NLP and other text-related areas of machine learning, text augmentation is an essential data augmentation technique. Text data can be transformed by rearranging sentences, shifting word locations, substituting words with closely related synonyms, adding random words, and removing random words.
Neural style transfer
An advanced method of data augmentation that breaks down images into smaller components is neural style transfer. It creates many images from a single image by using a sequence of convolution layers that separate an image’s style and context.
Adversarial training
An ML model has difficulties when pixel-by-pixel changes occur. To test the model’s perception of the image underlying, some examples overlay an image with an undetectable layer of noise. This approach is a proactive method of augmenting data that focuses on possible illegal access in the actual world.
What is the role of generative AI in data augmentation?
Because it makes it easier to create synthetic data, generative artificial intelligence is crucial to data augmentation. It facilitates the production of realistic data, increases data diversity, and protects data privacy.
Generative adversarial networks
The framework of generative adversarial networks (GAN) consists of two opposing core neural networks. The discriminator separates the real data from the synthetic samples after the generator creates samples of the fake data.
By concentrating on fooling the discriminator, GANs gradually increase the generator’s output. In order to provide data augmentation with extremely dependable samples that closely resemble the original data distribution, data that can deceive the discriminator is considered high-quality synthetic data.
Variation auto encoders
Neural networks such as variation auto encoders (VAE) can help expand the sample size of core data and lessen the need for laborious data collection. A decoder and an encoder are the two interconnected networks that make up VAEs. Sample photos are converted into an intermediate form by the encoder. Based on its comprehension of the original samples, the decoder uses the representation to generate images that are similar. Because VAEs can produce data that is remarkably similar to sample data, they can be used to increase diversity while preserving the original distribution of data.
