  In many scenarios, 1) data streams are generated in real time; 2) labeled
data 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) the
storage of historical streams is limited and model updating can only be
achieved based on a very short lookback window. This learning setting limits
the applicability and availability of many Machine Learning (ML) algorithms. We
generalize the learning task under such setting as a semi-supervised drifted
stream learning with short lookback problem (SDSL). SDSL imposes two
under-addressed challenges on existing methods in semi-supervised learning,
continuous learning, and domain adaptation: 1) robust pseudo-labeling under
gradual shifts and 2) anti-forgetting adaptation with short lookback. To tackle
these challenges, we propose a principled and generic generation-replay
framework to solve SDSL. The framework is able to accomplish: 1) robust
pseudo-labeling in the generation step; 2) anti-forgetting adaption in the
replay step. To achieve robust pseudo-labeling, we develop a novel pseudo-label
classification model to leverage supervised knowledge of previously labeled
data, unsupervised knowledge of new data, and, structure knowledge of invariant
label semantics. To achieve adaptive anti-forgetting model replay, we propose
to view the anti-forgetting adaptation task as a flat region search problem. We
propose a novel minimax game-based replay objective function to solve the flat
region search problem and develop an effective optimization solver. Finally, we
present extensive experiments to demonstrate our framework can effectively
address the task of anti-forgetting learning in drifted streams with short
lookback.
