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In many scenarios, 1) data streams are generated in real time; 2) labeleddata are expensive and only limited labels are available in the beginning; 3)real-world data is not always i.i.d. and data drift over time gradually; 4) thestorage of historical streams is limited and model updating can only beachieved based on a very short lookback window. This learning setting limitsthe applicability and availability of many Machine Learning (ML) algorithms. Wegeneralize the learning task under such setting as a semi-supervised driftedstream learning with short lookback problem (SDSL). SDSL imposes twounder-addressed challenges on existing methods in semi-supervised learning,continuous learning, and domain adaptation: 1) robust pseudo-labeling undergradual shifts and 2) anti-forgetting adaptation with short lookback. To tacklethese challenges, we propose a principled and generic generation-replayframework to solve SDSL. The framework is able to accomplish: 1) robustpseudo-labeling in the generation step; 2) anti-forgetting adaption in thereplay step. To achieve robust pseudo-labeling, we develop a novel pseudo-labelclassification model to leverage supervised knowledge of previously labeleddata, unsupervised knowledge of new data, and, structure knowledge of invariantlabel semantics. To achieve adaptive anti-forgetting model replay, we proposeto view the anti-forgetting adaptation task as a flat region search problem. Wepropose a novel minimax game-based replay objective function to solve the flatregion search problem and develop an effective optimization solver. Finally, wepresent extensive experiments to demonstrate our framework can effectivelyaddress the task of anti-forgetting learning in drifted streams with shortlookback.

Semi-supervised Drifted Stream Learning with Short Lookback