Abstract: A computer implemented method of training an encoder to extract features from sensor data comprises generating a plurality of training examples, each training example comprising at least two data representations of a set of sensor data, the at least two data representations related by a transformation parameterized by at least one numerical transformation value; and training the encoder based on a self-supervised regression loss function applied to the training examples. The encoder extracts respective features from the at least two data representations of each training example, and at least one numerical output value is computed from the extracted features. The self-supervised regression loss function encourages the at least one numerical output value to match the at least one numerical transformation value parameterizing the transformation.

Abstract: It A computer implemented method of training an encoder to extract features from sensor data comprises training a machine learning (ML) system based on a self-supervised loss function applied to a training set, the ML system comprising the encoder. The training set comprises first data representations and corresponding second data representations, wherein the encoder extracts features from each first and second data representation, and wherein the self-supervised loss function encourages the ML system to associate each first data representation with its corresponding second data representation based on their respective features.

Abstract: A computer implemented method of training an encoder to extract features from sensor data comprises training a machine learning (ML) system based on a self-supervised loss function applied to a training set, the ML system comprising the encoder. The training set comprises sets of real sensor data and corresponding sets of synthetic sensor data. The encoder extracts features from each set of real and synthetic sensor data, and the self-supervised loss function encourages the ML system to associate each set of real sensor data with its corresponding set of synthetic sensor data based on their respective features.