  Continuous learning from an immense volume of data streams becomes
exceptionally critical in the internet era. However, data streams often do not
conform to the same distribution over time, leading to a phenomenon called
concept drift. Since a fixed static model is unreliable for inferring
concept-drifted data streams, establishing an adaptive mechanism for detecting
concept drift is crucial. Current methods for concept drift detection primarily
assume that the labels or error rates of downstream models are given and/or
underlying statistical properties exist in data streams. These approaches,
however, struggle to address high-dimensional data streams with intricate
irregular distribution shifts, which are more prevalent in real-world
scenarios. In this paper, we propose MCD-DD, a novel concept drift detection
method based on maximum concept discrepancy, inspired by the maximum mean
discrepancy. Our method can adaptively identify varying forms of concept drift
by contrastive learning of concept embeddings without relying on labels or
statistical properties. With thorough experiments under synthetic and
real-world scenarios, we demonstrate that the proposed method outperforms
existing baselines in identifying concept drifts and enables qualitative
analysis with high explainability.
