Generalized Incremental Learning under Concept Drift across Evolving Data Streams

--Real-world data streams exhibit inherent non-stationarity characterized by concept drift, posing significant challenges for adaptive learning systems. While existing methods address isolated distribution shifts, they overlook the critical co-evolution of label spaces and distributions under limited supervision and persistent uncertainty. T o address this, we formalize Generalized Incremental Learning under Concept Drift (GILCD), characterizing the joint evolution of distributions and label spaces in open-environment streaming contexts, and propose a novel framework called Calibrated Source-Free Adaptation (CSF A). First, CSF A introduces a training-free prototype calibration mechanism that dynamically fuses emerging prototypes with base representations, enabling stable new-class identification without optimization overhead. It integrates sharpness-aware perturbation loss optimization with surrogate gap minimization, while employing entropy-based uncertainty filtering to discard unreliable samples. This mechanism ensures robust distribution alignment and mitigates generalization degradation caused by uncertainties. Therefore, CSF A establishes a unified framework for stable adaptation to evolving semantics and distributions in open-world streaming scenarios. Extensive experiments validate the superior performance and effectiveness of CSF A compared to state-of-the-art approaches. N machine learning, the conventional training process typically relies on pre-collected datasets. It assumes that training and test data ideally adhere to the same distribution, facilitating the effective generalization of trained models to test data. However, real-world data are often continuously and sequentially generated over time, which is referred to as data streams or streaming data [1], [2]. These data streams are susceptible to changes in their underlying distribution, a phenomenon known as concept drift [3].