AugLy: A new data augmentation library to help build more robust AI models
We are open-sourcing AugLy, a new Python library that will help AI researchers use data augmentations to evaluate and improve the robustness of their machine learning models. Augmentations can include a wide variety of modifications to a piece of content, ranging from recropping a photo to changing the pitch of a voice recording. It’s important to build AI that isn’t fooled by these changes. AugLy helps by providing sophisticated data augmentation tools to create samples to train and test different systems.
AugLy is a novel open source data augmentation library that combines multiple modalities: audio, image, video, and text, which is increasingly important in many AI research fields. It offers more than 100 data augmentations focused on things that real people on the Internet do to images and videos on platforms like Facebook and Instagram. For example, this includes overlaying text, emoji, & screenshot transforms.
Combining different modalities -- such as text and images or audio and video -- using real-world augmentations can help machines better understand complex content . The meaning of the text phrase “love the way you smell today,” for example, changes entirely when overlayed on an image of a skunk. It’s also more akin to the way people take in information from multiple senses in order to learn about the world around them. As data sets and models become more multimodal, it’s useful to be able to transform all of a project’s data under one unified library and API.
