Novelty Detection and Learning from Extremely Weak Supervision

By Eduardo Almeida Soares, Plamen Parvanov Angelov. 2019

5 citations recorded by the source corpus.

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

Subfields: Artificial Intelligence

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Citations in the corpus

Towards Explainable Deep Neural Networks (xDNN) (2019, background): “…- top accuracy combined with an automatic approach for feature extraction for complex problems, such as image classiﬁcation, we try to address its deﬁciencies such as the lack of explainability [7], computational burden, power and energy resources required, ability to self-adapt and evolve [10].”