Semi-supervised statistical learning systems using a posterior external quality estimation
Evgeny A. Shvets, Lev Teplyakov, Ekaterina Pavlova · 2019
With the development of Artificial Neural Networks (ANNs), they are becoming key components in many computer vision systems. However, to train ANNs or other machine learning programs it is necessary to create large and representative datasets, which can be a costly, hard and sometimes even impossible task. Another important problem with such programs is the data drift: in real-world applications input data can change with time, and the quality of a machine learning system trained on the fixed dataset may deteriorate. To combat these problems, we propose a model of ANN-based machine learning classification system that can be trained during its exploitation. The system both classifies input examples and performs training on the data gathered during its operation. We assume that besides ANN there is an external module in the system that can estimate confidence of the answers given by ANN. In this paper we consider two examples of such external module: a separate, uncorrelated classifier and a module that estimates ANN output by searching recognized words in a dictionary. We conduct numerical experiments to study the properties of the proposed system and compare it to ANNs trained offline.