Self-Supervised Learning from Incrementally Drifting Data Streams
Valerie Vaquet, Jonas Vaquet, Fabian Hinder, Kleanthis Malialis, Christos G. Panayiotou, Marios M. Polycarpou, Barbara Hammer · 2024
Supervised online learning relies on the assumption that ground truth information is available for model updates at each time step.As this is not realistic in every setting, alternatives such as active online learning, or online learning with verification latency have been proposed.In this work, we assume that no label information is available after intitial training.We argue that provided we can characterize the expected concept drift as incremental drift, we can rely on a self-labeling strategy to keep updated models.We derive a k-NN-based self-labeling online learner implementing the presented self-supervised scheme and experimentally show that this is an option for learning from incrementally drifting data streams in the absence of label information.