What is data augmentation in data preprocessing?

With custom AI implementations, the quality of your documentation makes all the difference between a high-performing instance and a mediocre one. Unfortunately, many enterprise knowledge bases have a lot of holes in their data.

This is where data augmentation comes into play. As a step in the data preprocessing pipeline, it allows us to artificially expand the size of a dataset by creating modified versions of existing data. By using data augmentation, we can effectively enhance the diversity of data available to fine-tune an LLM, instantiate a RAG, or otherwise spin up some sort of copilot or in-house AI expert for your organization. More robust data leads to more robust outcomes.

Data augmentation addresses critical AI challenges, such as overfitting and poor generalization, which can hinder a model's effectiveness.

• Preventing overfitting: Overfitting happens when a model performs well on training data but fails to generalize to new, unseen data. By introducing variations in the training data through augmentation, the model learns to recognize patterns more broadly rather than memorizing specific examples.
• Enhancing model generalization: Models trained on augmented datasets tend to perform better on real-world data. This is because the variations introduced during augmentation mimic the sorts of noise and changes that might be encountered in actual scenarios.

Data augmentation is widely used in fields such as image processing (e.g., for facial recognition), natural language processing (NLP) (e.g., for sentiment analysis), and speech recognition (e.g., for voice command systems).

Data augmentation techniques vary depending on the type of data being processed. Here are some of the most common methods:

Data augmentation for images

• Geometric transformations: These include rotating, scaling, translating, flipping, and cropping images. For example, rotating an image of a cat by 15 degrees still results in a recognizable cat, providing the model with a new perspective.
• Color space transformations: Adjustments such as altering brightness, contrast, saturation, and hue urges the model to learn to recognize objects under different lighting conditions.
• Noise addition: Adding random noise to an image can make the model more resilient to imperfections and variations in input data.
• Synthetic data generation: Techniques like generative adversarial networks (GANs) can create entirely new images based on existing ones, expanding the dataset with realistic yet novel examples.

Data augmentation for text

• Synonym replacement: Replacing certain words with their synonyms to create variations in sentences without altering their meaning. For example, "The cat sat on the mat" could be transformed into "The feline sat on the mat."
• Random insertion, deletion, and swap: Small changes—inserting additional words, deleting existing ones, or swapping the order of words—can introduce diversity in text data.
• Back translation: Translating a sentence into another language and then back to the original language can generate variations that maintain the original meaning but differ in structure or word choice.

Data augmentation for audio

• Time stretching and compression: Altering the speed of the audio without changing the pitch, which can help the model learn to recognize speech or sounds at different speeds.
• Pitch shifting: Modifying the pitch of the audio while keeping the duration constant. This technique is particularly useful in speech and music recognition tasks.
• Noise addition: Introducing background noise into audio samples to make models more robust to real-world audio variations.