Novelty Detection and Learning from Extremely Weak Supervision

TL;DR

This paper introduces xClass, an autonomous, unsupervised method for novelty detection and learning from minimal initial labeled data, capable of discovering new classes and feature sub-selection.

Contribution

The proposed xClass algorithm enables fully autonomous class discovery and learning from extremely weak supervision with automatic feature sub-selection.

Findings

Higher precision on challenging datasets

Achieved with minimal labeled data and weak supervision

Generated interpretable, efficient models

Abstract

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 features used, through extremely weak and weak supervision.