Self-Supervised Learning from Incrementally Drifting Data Streams
Creators
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1.
Bielefeld University
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2.
University of Cyprus
Description
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.
Files
ES2024-49.pdf
Files
(1.7 MB)
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