Data Augmentation: A Guide

Data augmentation is the process of artificially expanding a data set by building on existing data and creating modified versions of it.

What does that mean? Well, grab your phone and try to find the worst photo you’ve ever taken of your pet, or any animal really. Maybe it was blurry, taken from a terrible angle or the flash went off at the wrong moment. While the photo may be distorted, if you share it with a computer it can immediately tell you exactly what animal it is, down to the breed. How? That’s data augmentation. 

What Is Data Augmentation?

Data augmentation is the method for supplementing incomplete data sets by providing missing data points in order to increase the data set’s quality for analysis.

It’s like giving your data set a makeover to improve the chances of accuracy while making predictions. It can be used to perform simple flips and rotations in images to paraphrasing in text. Data augmentation aims to improve the utility of existing data by creating it in new, diverse forms.

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How Does Data Augmentation Work?

Data augmentation works by applying controlled transformations to the original data, producing new, slightly altered samples that retain the essential characteristics of the original. These transformations vary depending on the type of data — images, text or audio — and can be applied individually or in combination to maximize data set diversity.

Rather than collecting thousands of additional examples, data augmentation makes way for an enormous amount of enrichment to data's value by generating variations that preserve the core information while introducing data diversity that can be readily used. It uses deep learning to generate new data points by artificially expanding the size and diversity of a data set.

These modifications can be as simple as flipping or cropping an image or as complex as generating entirely new data using deep learning models. The fundamental idea is to expose models to a wider range of scenarios, making them more robust and better at generalizing the unseen data. 

Its techniques vary widely depending on the type of data being augmented. 

Data Augmentation for Images

Image augmentation is the process of enhancing and altering the pixel data through rotation, cropping, brightness changes, flipping, etc., in order to provide variation so that the model can handle the real world diversity. This process increases the diversity of the data set to train computer vision models for image classification. 

Let’s understand this with a simple example. 

If you need to know what the endangered vaquita looks like, the system needs to be fed with the real-life images of it. But since it's a rare species, there are not many images of it.This is where image augmentation steps in. 

Imagine you only have a few photos of the critically endangered marine mammal, and that's not completely enough for a computer to learn what it looks like from all angles or under different conditions and circumstances.

Image augmentation helps here. We take those few photos and make lots of new ones. To enable this, we might rotate them, flip them, zoom in, zoom out, increase the hue or saturation, blend and mix multiple images of them, change the brightness of the image or even crop it. It's like showing the computer the same Vaquita from different angles or in different light, as can be seen in the image below.

This trick provides a closer-to-360-degree look and helps the model learn better, even with very little real data. It also makes the model much smarter at finding Vaquitas in any new picture.

Data Augmentation for Text

Though text data augmentation can be more challenging due to the complexity of language, it follows a similar process as augmented images. 

Like images when they are cropped and flipped, etc, text augmentation allows for changes in text structure through using several synonyms of one word, inserting and erasing random words from sentences, translating sentences into another language and then back to the original language, shuffling sentence order and swapping the position of words in sentences, etc. All these permutations are an attempt to introduce as much variation in the data set as possible. Let’s see this with the help of a simple sentence.

“Evolving technology continues to redefine how we communicate, work and live.” 

This can be modified by replacing words with synonyms viz-a-viz advancing for evolving, transform for redefine, or by inserting descriptors like rapidly or digital to enrich context and detail.

Other techniques which include deletion can be exemplified by shortening the original sentence to “Changing technology is molding how we communicate and work”, or back translation through other languages to introduce structural variation. While word swapping can alter flow, sentence shuffling within a paragraph tests contextual placement. These methods help models generalize better by exposing them to varied yet semantically equivalent data.

Text augmentation fundamentally assists language models in understanding that “The meal was delicious” and “The food tasted amazing or sumptuous” convey essentially the same sentiment.

Data Augmentation for Audio

For audio and speech data, augmentation techniques help models handle real-world variability by changing and altering the pitch or frequency of audio signals, adding background noise to audio signals, mixing the audio with other sounds, stretching or compressing the duration of audio signals and temporally warping the audio signals. 

Warping the audio signals allows the model to stretch or compress the audio, by making it play faster or slower, without necessarily changing its pitch. Warping for augmentation is generally used to synchronize audio clips with different tempos, correct timing issues, and/or creatively alter rhythms and melodies.

These methods help models become resilient to different accents, pronunciations, and background environments.

For instance, take a voice recording saying: “Welcome to the future of technology.” You can apply pitch shifting, time stretching or adding background noise like light static or ambient sounds.

Some other methods include simulating different room environments also commonly known as reverberation, altering frequency balance or equalization or random cropping. These introductions and modifications help train robust AI models, such as voice assistants or speech recognizers to perform well under various real-world audio conditions without requiring massive original data sets. 

Why Use Data Augmentation?

Data augmentation prepares businesses for the real world by ensuring it has seen enough variations to handle unexpected results and a wide array of possibilities that they may face along the way. It’s a creative, dynamic structure purposely designed to build smarter, more resilient models. 

Enhances Model Performance

By increasing data diversity and developing holistic data sets, models learn more robust and general features, thereby leading to better accuracy on new data.

Reduces Overfitting

Augmented data prevents models from memorizing the training set, encouraging generalization. When overfitting is reduced, the model tends to recognize specific phrases in a sentence and also understands the underlying language structure.

Mitigates Data Scarcity 

When collecting large, diverse data sets is highly impractical, difficult or costly, data augmentation  helps expand the existing data set to make the most of what’s available.

Increases Data Privacy

Data augmentation increases data privacy by generating synthetic variations that reduce reliance on sensitive or original personal data. This helps train models effectively without running the risk of exposing real user information directly.

If one were to put it lucidly, data augmentation’s core objective is to ameliorate the performance and generalization of machine learning models by exposing them to a wider range of variations and scenarios during training. 

Data Augmentation Real-World Examples

Image Classification

In computer vision, image augmentation is important for tasks like agricultural monitoring. For instance, augmentations like zooming, rotation, and hue and saturation adjustments help models identify plant diseases under different angles, lighting or stages of infection. It can also diagnose pest damage and nutrient deficiencies from the plant photos.

Self-driving cars, on the other hand, use extensively augmented image data to recognize road signs, pedestrians and obstacles in all weather conditions, lighting situations and partially obscured views.

Speech Recognition

Speech recognition models score from audio augmentation by training on voices with added noise or altered pitch, making them highly aware and perceptive to different speakers and environments. 

One example is that of virtual assistants. Siri and Alexa employ audio augmentation to understand commands spoken in noisy environments, by different age groups, and with various accents and voice modulations, all without having to record every possible combination.  

Natural Language Processing Tasks

Text augmentation is aimed at producing translations or neatly spun sentences, thereby improving performance on diverse linguistic inputs. This can be seen in how sentiment analysis tools use text augmentation to recognize positive, negative or neutral opinions expressed in countless different ways, making them more adaptable to the creative ways humans express themselves. 

This analysis largely runs on the belief that a model trained on augmented data is better at recognizing sentiment even when the wording differs from the training set.

Limitations and Considerations of Data Augmentation

Despite its widespread popularity and adaptability, data augmentation isn’t perfect and has its limitations. 

• The first and major limitation is that not all augmentations preserve meaning. This implies inverting a “W” to become “M” or flipping “b” to “d” fundamentally changes its identity.
• Augmentation brings the risk of synthetic data introducing biases or artifacts not present in real data. In other words, poorly chosen transformations can introduce unrealistic or misleading data, harming model performance.
• Real-time augmentation during training can result in significant increase of computational costs and demands.
• Some domains require extremely careful augmentation to maintain validity. This makes the process strenuous and demanding.
• Over or excessive augmentation, as one may call it, may yield little benefit or even degrade performance if the synthetic data diverges too far from reality.

The key is understanding which transformations make sense for your specific problem and data type and then making a choice about the sort of augmentation needed.

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Tools and Popular Libraries for Data Augmentation

There are several deep learning frameworks, such as PyTorch, Keras and TensorFlow that provide functions for augmenting data, principally image datasets. AugLy (Meta) is an open source data augmentation library and is multi-modal in nature.

• For Images: TensorFlow (ImageDataGenerator), PyTorch (torchvision.transforms), Albumentations, Keras, imgaug, and AugLy.
• For Text: TextAttack, NLPAug, EDA (easy data augmentation), Parrot Paraphraser, BackTranslation, and AugLy.
• For Audio: Audiomentations, SpecAugment, WavAugment, Torchaudio and AugLy.

These libraries provide ready-to-use functions for common augmentation techniques and can be integrated into most machine learning workflows.

Frequently Asked Questions