Synthetic Data for Superior AI Solutions

Neonode’s data training methodologies provide accurate and adaptive data models, that can rapidly meet real world change.

Data is the core of any machine learning application. For a machine learning model to learn to recognize patterns, objects or behaviors, it needs to be trained on large amounts of data that represent what the model is supposed to learn.

For computer vision tasks, training data consists of images together with ground truth annotations. If you’re teaching a model to find faces, these annotations could tell the model if there is a face in the training image and where in the image the face is located. When AI behaves unexpectedly, the root cause is often found in the training data used for teaching the machine learning model. For example, if the ‘face dataset’ doesn’t include any pictures of faces in the profile, the final model will have a hard time recognizing human heads.

In this white paper, Agnes Jernström, explains how Neonode uses synthetic data to train our neural networks to ensure the highest possible accuracy.