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In this paper we offer a method and algorithm, which make possible fullyautonomous (unsupervised) detection of new classes, and learning following avery parsimonious training priming (few labeled data samples only). Moreover,new unknown classes may appear at a later stage and the proposed xClass methodand algorithm are able to successfully discover this and learn from the dataautonomously. Furthermore, the features (inputs to the classifier) areautomatically sub-selected by the algorithm based on the accumulated datadensity per feature per class. As a result, a highly efficient, lean,human-understandable, autonomously self-learning model (which only needs anextremely parsimonious priming) emerges from the data. To validate our proposalwe tested it on two challenging problems, including imbalanced Caltech-101 dataset and iRoads dataset. Not only we achieved higher precision, but, moresignificantly, we only used a single class beforehand, while other methods usedall the available classes) and we generated interpretable models with smallernumber of features used, through extremely weak and weak supervision.

Novelty Detection and Learning from Extremely Weak Supervision