In machine learning, synthetic data can offer real performance improvements

Models trained on synthetic data can be more accurate than other models in some cases, which could eliminate some privacy, copyright, and ethical concerns from using real data.

Teaching a machine to recognize human actions has many potential applications, such as automatically detecting workers who fall at a construction site or enabling a smart home robot to interpret a user’s gestures.

To do this, researchers train machine-learning models using vast datasets of video clips that show humans performing actions. However, not only is it expensive and laborious to gather and label millions or billions of videos, but the clips often contain sensitive information, like people’s faces or license plate numbers. Using these videos might also violate copyright or data protection laws. And this assumes the video data are publicly available in the first place — many datasets are owned by companies and aren’t free to use.

So, researchers are turning to synthetic datasets. These are made by a computer that uses 3D models of scenes, objects, and humans to quickly produce many varying clips of specific actions — without the potential copyright issues or ethical concerns that come with real data.

But are synthetic data as “good” as real data? How well does a model trained with these data perform when it’s asked to classify real human actions? A team of researchers at MIT, the MIT-IBM Watson AI Lab, and Boston University sought to answer this question. They built a synthetic dataset of 150,000 video clips that captured a wide range of human actions, which they used to train machine-learning models. Then they showed these models six datasets of real-world videos to see how well they could learn to recognize actions in those clips.

The researchers found that the synthetically trained models performed even better than models trained on real data for videos that have fewer background objects.

This work could help researchers use synthetic datasets in such a way that models achieve higher accuracy on real-world tasks. It could also help scientists identify which machine-learning applications could be best-suited for training with synthetic data, in an effort to mitigate some of the ethical, privacy, and copyright concerns of using real datasets.

“The ultimate goal of our research is to replace real data pretraining with synthetic data pretraining. There is a cost in creating an action in synthetic data, but once that is done, then you can generate an unlimited number of images or videos by changing the pose, the lighting, etc. That is the beauty of synthetic data,” says Rogerio Feris, principal scientist and manager at the MIT-IBM Watson AI Lab, and co-author of a paper detailing this research.[…]