Self-supervised learning enables a vectorized mapping of unlabeled datasets. When dealing with large visual datasets, self-supervised techniques remove those datapoints that are biased or redundant, which
would otherwise damage the AI. In autonomous driving, as companies gather petabytes of visual data, supervised learning enables them to identify the most relevant data points, thus increasing deployment speed while decreasing costs.