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Top Data Augmentation Techniques: Ultimate Guide for 2024

Top Data Augmentation Techniques: Ultimate Guide for 2024Top Data Augmentation Techniques: Ultimate Guide for 2024

Data augmentation techniques generate different versions of a real dataset artificially to increase its size. Computer vision and natural language processing (NLP) models use data augmentation strategy to handle with data scarcity and insufficient data diversity.

Data-centric AI/ML development practices such as data augmentation can increase accuracy of machine learning models. According to an experiment, a deep learning model after image augmentation performs better in training loss (i.e. penalty for a bad prediction) & accuracy and validation loss & accuracy than a deep learning model without augmentation for image classification task.

Data augmentation techniques in computer vision

There are geometric and color space augmentation methods for images to create image diversity in the model. It is easy to find many coding examples for these augmentation transformations from open source libraries and in articles on the topic.

Adding noise

For blurry images, adding noise on the image can be useful. By “salt and pepper noise”, the image looks like consisting of white and black dots.

Adding noise to images as a data augmentation technique.
soruce: medıum


A section of the image is selected, cropped and then resized to the original image size. 

Cropping images as a data augmentation technique.
Source: Github


The image is flipped horizontally and vertically. Flipping rearranges the pixels while protecting the features of the image. Vertical flipping is not meaningful for some photos, but it can be useful in cosmology or for microscopic photos.


The image is rotated by a degree between 0 and 360 degree. Every rotated image will be unique in the model.

Rotating images as a data augmentation technique.
Source: Medium


The image is scaled outward and inward. An object in new image can be smaller or bigger than in the original image by scaling.

Scaling an image outward and inward as a data augmentation technique.
Source: Medium


The image is shifted into various areas along the x-axis or y-axis, so neural network looks everywhere in the image to capture it.

Shifting images on areas along x-axis and y-axis as a data augmentation technique.


The brightness of the image is changed and new image will be darker or lighter. This technique allows the model to recognize image in different lighting levels.

Changing the brightness of images as a data augmentation technique.


The contrast of the image is changed and new image will be different from luminance and colour aspects. The following image’s contrast is changed randomly.

Changing the contrast of images as a data augmentation technique.

Color Augmentation

The color of image is changed by new pixel values. There is an example image which is grayscale.

Changing the colors of images with new pixel values as a data augmentation technique.


Saturation is depth or intensity of color in an image. The following image is saturated with data augmentation method.

Changing the saturation of images as a data augmentation technique.

You can also check check our article on synthetic data for computer vision.

Data augmentation techniques in natural language models

Data augmentation techniques are applied on character, word and text levels.

Easy Data Augmentation (EDA) Methods

EDA methods include easy text transformations, for example a word is chosen randomly from the sentence and replaced with one of this word synonyms or two words are chosen and swapped in the sentence. EDA techniques examples in NLP processing are

  • Synonym replacement
  • Text Substitution (rule-based, ML-based, mask-based and etc.)
  • Random insertion
  • Random swap
  • Random deletion
  • Word & sentence shuffling

Back Translation

A sentence is translated in one language and then new sentence is translated again in the original language. So, different sentences are created.

Translating a sentence to another language and translating it back to the original language as a data augmentation technique.
Source: Medium

Text Generation

A generative adversarial networks (GAN) is trained to generate text with a few words.

Using generative algorithms such as GANs to generate new text.
Source: Medium

Developers can optimize natural language models by training them on web data which contains large volumes of human speech, languages, syntaxes, and sentiments.

Data augmentation techniques for audio data

Audio data augmentation methods include cropping out a portion of data, noise injection, shifting time, speed tuning changing pitch, mixing background noise and masking frequency.

Various ways of audio augmentation such as cropping, changing speed, injecting noise and masking frequency.
Source: Github

Advanced data augmentation techniques

Advanced data augmentation methods are commonly used in deep learning domain. Some of these techniques are

  • Adversarial training
  • Neural style transfer
  • Generative adversarial networks (GANs) based augmentation

For more, feel free to read our articles on deep learning data augmentation and GANs for synthetic data.

Data augmentation libraries

There are libraries for developers, such as Albumentations, Augmentor, Imgaug, nlpaug, NLTK and spaCy. These libraries include geometric transformation & color space transformations functions, Kernel filters (i.e. image processing function for sharpening and blurring) and other text transformations. Data augmentation libraries use different deep learning frameworks, for example, Keras, MxNet, PyTorch and TensorFlow.

If you are ready to use data augmentation in your firm, we prepared data driven lists of companies. However, we don’t yet have a list exclusively for data augmentation libraries yet. Most of the time, this functionality is provided as part of more comprehensive software packages (i.e. deep learning software):

If you need help in choosing vendors who can help you get started, let us know:

Find the Right Vendors

This article was drafted by former AIMultiple industry analyst Ayşegül Takımoğlu.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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