Data augmentation is a technique used to artificially fabricate and increase the size and diversity of training datasets. It involves modifying existing data to create new ones that retain original information while introducing new realistic variations.

Performing data augmentation is beneficial for AI systems for several reasons:

Increased data variety:

• By transforming data and introducing variations, artificial intelligence models generalize better when they face new unseen data.
• It results in better coverage over a wide range of scenarios by improving the AI model’s ability to handle different types of input data.
• It helps prevent overfitting, which occurs when an AI model becomes too specialized in capturing variations present in a dataset, thus failing to generalize well when facing new, unseen data.

Robustness:

• Data augmentation makes the AI model more robust when facing noise, variations and outliers in the input data.

Overcoming data scarcity:

• For many artificial intelligence applications, obtaining a large, labelled training dataset can be time-consuming, expensive, and infeasible.
• With data augmentation, we can create training data from existing data, increasing the amount available for training without performing additional manual labelling.

Using data augmentation techniques, AI models display improved performance when facing tasks such as image classification, object detection, and natural language processing.

Depending on the type of data and the desired variations, we select the type of data augmentation technique to be applied. The following are some regularly used techniques:

Image data augmentation:

• Rotation: Rotating an image by a certain degree.
• Translation: Sliding an image horizontally or vertically.
• Scale: Resize the image to different sizes.
• Flip: Flipping the image horizontally or vertically.
• Crop: Reducing the edges of an image to extract a small part of the original image.
• Image Shear: To shear the original image in a defined manner.
• Zoom: Zooming in or out of the picture.
• Colour Jitter: Adjust the brightness, contrast, saturation or hue.
• Gaussian Noise: Adding random Gaussian noise to the image.

Text data augmentation:

• Synonyms Replacement: Replacing words with their synonyms.
• Random Insert: Adding words at random positions.
• Random Delete: Deleting words at random from a sentence
• Random Swap: Swapping the placement of any two words in a sentence.
• Text Mask: Replacing certain words or characters with a mask token.

Audio data augmentation:

• Pitch Shift: Changing the pitch of the audio.
• Time Stretch: Speeding or slowing down the audio.
• Background Noise: Adding background noise to the audio.
• Amplitude Scale: Adjusting the volume of the audio.
• Time Shift: Shifting the audio in time.
• Frequency Mask: Applying frequency masking to the audio spectrogram.
• Time Mask: Applying time masking to the audio spectrogram.

Video data augmentation:

• Frame Sample: Select a random set of frames from a video.
• Frame Flip: Flipping portions of a video horizontally or vertically.
• Colour Jitter: Adjusting the colour and brightness of frames.
• Spatial Crop and Resize: Extracting a small part of the video and resizing it.
• Speed Perturbation: Speeding up or slowing down the video playback.

In conclusion, data augmentation is a powerful technique that enhances the performance and generalization of artificial intelligence models by introducing variations to data that mimic real-world scenarios.

It plays a crucial role in overcoming the limitations of limited data and diminishes the risk of overfitting, thus enabling more accurate and reliable predictions in various domains and applications.