The What and Why of Annotation Augmentation: Three Key Benefits
Everybody knows dataset bias exists.
What’s more, most folks are very adept at identifying it. But once your bias is defined, how do you act on it? For Innotescus users, the answer will soon be augmentation. We’re adding augmentation in our platform to make sure anyone can bust bias when they find it.
But what is augmentation? And why is it such a powerful method? This blog will tell you exactly that, as well as three key ways that augmentation improves your workflow.
What’s inside this article:
What is augmentation?
Augmentation is the modifying of pre-existing data in order to increase representation of certain classes, features, or dataset size as a whole. There are plenty of simple transformations that work as augmentations (scale, rotate, add noise, adjust hue…) and each has their own bias-busting benefits. But overall, augmentation makes more information from what is already there.
This differs from synthetic data, the product of a process that creates wholly artificial data. As a result, augmented data tends to be richer than synthetic data; imagine training an auto-pilot program with a flight simulator instead of real images or videos taken from a plane. You can imagine the detail maintained in real-world data that a 3d-rendered image would lack. While synthetic techniques create new data, augmentation creates more, unique versions of existing data.
Why do I need augmentation?
Would you rather train a model on 1,000 pictures of one class or 100,000? The superficial answer is always more. Data scientists are quick to find the biggest and least expensive data options, and with myriad free datasets available, who can blame them?
But the reality tends to be: more data=more problems. After a certain point, more data starts to worsen model performance. Massive datasets can be very hard to wrangle. The more information your project runs on, the harder it is to implement quality assurance measures, and the more inaccurate, unrelated, or uncurated data points your model learns from, the less “focused” model performance becomes.
So does that mean we’re stuck with only 1,000 pictures of one class? Thanks to augmentation, no. Using augmentation, scientists can scale smaller datasets with higher quality. Instead of spinning your wheels in data scarcity mud, you can use simple transformations to exponentially increase your dataset.
But make sure you augment wisely; augmentation can propagate bias just as easily as it can eliminate it. Make sure you’re amplifying the annotations that will help your overall model performance.
Three key benefits of data augmentation
1. Increase dataset size without increasing your budget…
Augmentation is almost always cheaper than collecting new data. But this process is more than just inexpensive. Using augmentation, you can maintain and ultimately improve your dataset diversity.
2. …or decreasing data quality
Augmentation preserves annotations from the original and transforms them along with the underlying data. Any QA work you’ve performed are amplified, making the smallest annotation corrections pay dividends.
What’s more, effective augmentation makes models more generalizable. How? Not only can your model recognize imperfections on an automotive assembly line, it knows that dents can happen at different angles and on different color vehicles, even if it had never “seen” that angle or that color vehicle before.
3. Target bias by pretty much any criteria
You can’t talk about data without talking about bias, and for good reason! We create bias the moment we give our models and our data purpose. But on an actionable level, bias is when our models lack the necessary information to treat future subjects correctly. This can look like overrepresentation of classes or features, omission of certain characteristics, or, really, anything that keeps a model from correctly responding to future situations. It can be caused by lighting, size, image orientation, even labels written in the margins. Augmentation is one simple way to increase representation and balance according to any criteria.
Ultimately, you and your team can save time and resources by curating smaller, high-quality datasets instead of larger, unregulated ones. Augmentation is, to us, one of the best ways you can do it.
Talk to our sales team to figure out how our Augmentation feature can be part of your next data-centric solution.
We are a group of scientists, engineers, and entrepreneurs with a vision for better AI. With backgrounds primarily in Machine Learning and Computer Vision, the Innotescus team understands the importance of having full control over and insight into data used to train Machine Learning models.
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