Resilient Contrastive Pre-training under Non-Stationary Drift

The remarkable success of large-scale contrastive pre-training has been largely driven by by vast yet static datasets. However, as the scaling paradigm evolves, this paradigm encounters a fundamental challenge when applied to dynamic data streams characterized by concept drift - unpredictable changes in the underlying data distribution. This paper aims to advance robust pre-training under such…