Deep Self-Supervised Learning Models for Automotive Systems

Supervised learning, unsupervised learning & reinforcement learning are the three basic learning techniques for training machine learning and artificial intelligence models. Deep learning models can be supervised or unsupervised. In auto industry, the deep learning applications use the supervised learning technique. Models trained with the unsupervised learning technique produce generalized results. It requires a huge set of tagged/labeled datasets to train these supervised deep learning networks. Self-supervised learning is a technique where the AI model learns the features from the training data, without tags or labels and tags the data by itself. This tagged/labelled data can be further used to train other AI models. This saves the cost of tagging the data. Tagging or labeling is a time-consuming activity, which also needs human effort to do the job. In self-learning or self-supervised learning, the activity of labeling is done automatically, which helps to save the cost and effort. On the other hand, are self-supervised models capable of making high-precision predictions which are needed in automobiles? There can be specific applications for which the self-supervised technique can be used, which can give accurate results. I will discuss different aspects of self-supervised learning, and their applications in the field of automobiles.