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What are the best data augmentation techniques for improving AI data quality?

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Data augmentation is a process of creating new and diverse data samples from existing ones by applying various transformations, such as flipping, rotating, cropping, scaling, adding noise, or changing colors. Data augmentation can help improve AI data quality by increasing the size, variety, and balance of the data set, reducing overfitting, enhancing generalization, and boosting performance. In this article, you will learn about some of the best data augmentation techniques for different types of AI data, such as images, text, audio, and tabular data.

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?? Nico Essi

CEO @ Zensara | ex-CTO | AI Scientist | 2x Founder
Ritwik Joshi ??

TEDx Speaker | IIMA | Co-Founder of Ability Advocacy | Technology Advisor @ GENIE AI & TestnTrack
Dr. Ivan Tankoyeu

AI Expert | Forbes Tech Council | Founder

1 Image augmentation

Image augmentation is one of the most widely used and effective data augmentation techniques for AI applications that involve computer vision, such as face recognition, object detection, or medical imaging. Image augmentation can apply various spatial, geometric, or pixel-level transformations to the original images, such as flipping, rotating, cropping, scaling, shearing, warping, adding noise, blurring, changing brightness, contrast, saturation, or hue. Image augmentation can help increase the diversity and robustness of the image data set, making the AI model more adaptable to different lighting, angles, orientations, or backgrounds.

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Dr. Ivan Tankoyeu

AI Expert | Forbes Tech Council | Founder
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Synthetic Data Generation approaches have gained popularity due to the significant improvement in the quality of generated images. These approaches have become instrumental in enhancing the performance and robustness of AI models in computer vision tasks. Some of the prominent techniques include: 3D Computer Graphics: This technique involves creating detailed 3D models of objects or scenes and rendering them into images or videos. Generative Adversarial Networks (GANs): GANs are a powerful tool in synthetic data generation, particularly for generating photorealistic images. Style Transfer and Image-to-Image Translation: These techniques involve modifying existing images to generate new ones with altered styles or characteristics.

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2 Text augmentation

Text augmentation is a data augmentation technique for AI applications that involve natural language processing, such as sentiment analysis, text classification, or machine translation. Text augmentation can apply various linguistic, semantic, or syntactic transformations to the original text, such as replacing words with synonyms, antonyms, or hypernyms, inserting or deleting words, swapping words, changing word order, paraphrasing sentences, or generating new sentences from existing ones. Text augmentation can help increase the size and richness of the text data set, making the AI model more capable of handling different vocabularies, styles, or contexts.

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Ritwik Joshi ??

TEDx Speaker | IIMA | Co-Founder of Ability Advocacy | Technology Advisor @ GENIE AI & TestnTrack
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?? Text augmentation is like giving our AI a language makeover! It's the trick to beefing up our language models with a diverse set of words and writing styles. Whether it's learning new vocab or understanding different ways people express themselves, text augmentation is the coach that ensures our models are ready for any linguistic challenge. In my view, it's not just about getting more words; it's about getting the right ones. Text augmentation helps our models become top-notch communicators by exposing them to a variety of linguistic styles.

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3 Audio augmentation

Audio augmentation is a data augmentation technique for AI applications that involve speech or sound recognition, such as speech-to-text, voice assistant, or music generation. Audio augmentation can apply various temporal, spectral, or acoustic transformations to the original audio, such as changing the speed, pitch, volume, or duration, adding noise, echo, or reverberation, mixing or overlaying audio samples, or converting audio formats. Audio augmentation can help increase the variety and realism of the audio data set, making the AI model more resilient to different noises, distortions, or variations.

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4 Tabular augmentation

Tabular augmentation is a data augmentation technique for AI applications that involve structured or numerical data, such as regression, classification, or clustering. Tabular augmentation can apply various statistical, mathematical, or logical transformations to the original tabular data, such as adding noise, outliers, or missing values, scaling, normalizing, or standardizing the data, generating new features from existing ones, or synthesizing new data samples from existing ones. Tabular augmentation can help increase the quantity and quality of the tabular data set, making the AI model more accurate and reliable.

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5 How to choose the best data augmentation technique

The best data augmentation technique depends on several factors, such as the type, size, and distribution of the data, the goal and complexity of the AI task, the availability and cost of the computational resources, and the evaluation and validation of the results. There is no one-size-fits-all solution for data augmentation, and different techniques may have different effects on different data sets and AI models. Therefore, it is important to experiment with different data augmentation techniques, compare their impacts, and optimize their parameters, to find the best data augmentation technique for your specific AI problem.

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?? Nico Essi

CEO @ Zensara | ex-CTO | AI Scientist | 2x Founder
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When choosing the best data augmentation technique, I've found the most important aspect to always be the preservation of relevant statistics within the data. Models ultimately learn to represent the relationship of their data points within their weights, and that is by creating a model of the inherent statistics. If you choose a data augmentation technique that corrupts important statistical relationships, you therefore eliminate the model's ability to capture and learn key features of the data.

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6 Data augmentation tools and libraries

Data augmentation can be implemented using various tools and libraries, depending on the programming language, framework, and platform you are using for your AI project. Some of the most popular and powerful data augmentation tools and libraries are:

- Augmentor : A Python library for image augmentation that supports a wide range of transformations, pipelines, and operations.

- NLPAug : A Python library for text and audio augmentation that supports various natural language processing and speech processing techniques.

- Albumentations : A Python library for image augmentation that is fast, flexible, and compatible with PyTorch, TensorFlow, Keras, and other frameworks.

- Imgaug : A Python library for image augmentation that supports both simple and complex transformations, such as geometric, color, blur, or weather effects.

- CTGAN : A Python library for tabular augmentation that uses conditional generative adversarial networks to synthesize realistic and diverse tabular data.

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Samira Ghodratnama, PhD

Sr AI/ML Scientist | Research Fellow | NLP, Search, Generative AI
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Effective data augmentation techniques include geometric transformations (rotation, flipping, cropping, resizing) for images, color space adjustments (brightness, contrast, saturation, hue changes), and random erasing for robustness against occlusions. Mixing images or blending with noise, alongside synthetic data generation using generative models like GANs, enhances diversity. In NLP, text augmentation methods such as synonym replacement, back translation, and sentence shuffling are beneficial. For time series data, techniques like time warping, slicing, and windowing are useful. Feature space augmentation, altering features directly, is applicable across data types, enhancing model robustness and preventing overfitting.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best data augmentation techniques for improving AI data quality?

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1 Image augmentation

2 Text augmentation

3 Audio augmentation

4 Tabular augmentation

5 How to choose the best data augmentation technique

6 Data augmentation tools and libraries

7 Here’s what else to consider

Artificial Intelligence

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What are the best data augmentation techniques for improving AI data quality?

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1 Image augmentation

2 Text augmentation

3 Audio augmentation

4 Tabular augmentation

5 How to choose the best data augmentation technique

6 Data augmentation tools and libraries

7 Here’s what else to consider

Artificial Intelligence

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