System and method for adaptive resource-efficient mitigation of catastrophic forgetting in continuous deep learning

Abstract

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support adaptive machine learning (ML) classification that mitigates effects of catastrophic forgetting while reducing overall resource requirements. To illustrate, a computing device may train first and second ML classifiers based on historical streamed data. The second ML classifier is trained to use continuous learning, and the first ML classifier is not. If data drift of a data stream is below a lower threshold, the data stream is provided as input to the first ML classifier to generate classification output (e.g., predictions). If the data drift is above the lower threshold, dynamic switching occurs and the data stream is provided as input to the second ML classifier instead of the first ML classifier to generate the classifier output. If the data drift is above an upper threshold, operations are performed to train new ML classifiers.