Rolling with the Punches: Resilient Contrastive Pre-training under Non-Stationary Drift

• URL: http://arxiv.org/abs/2502.07620v2
• Date: Mon, 19 May 2025 13:59:05 GMT
• Title: Rolling with the Punches: Resilient Contrastive Pre-training under Non-Stationary Drift
• Authors: Xiaoyu Yang, Jie Lu, En Yu,
• Abstract summary: A critical emerging challenge is the effective pre-training of models on dynamic data streams.<n>We first reveal that conventional contrastive pre-training methods are notably vulnerable to concept drift.<n>We propose Resilient Contrastive Pre-training (RCP), a novel method incorporating causal intervention.
• Score: 16.97188816362991
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The remarkable success of large-scale contrastive pre-training, fueled by vast and curated datasets, is encountering new frontiers as the scaling paradigm evolves. A critical emerging challenge is the effective pre-training of models on dynamic data streams characterized by concept drift, unpredictable changes in the underlying data distribution. This paper undertakes a foundational investigation of this issue. We first reveal that conventional contrastive pre-training methods are notably vulnerable to concept drift, leading to significant biases in the learned feature space of pre-trained models. To systematically analyze these effects, we construct a structural causal model that elucidates how drift acts as a confounder, distorting learned representations. Based on these causal insights, we propose Resilient Contrastive Pre-training (RCP), a novel method incorporating causal intervention. RCP introduces a causally-informed objective designed to mitigate drift-induced biases by leveraging targeted interventions. RCP is designed for simple and scalable implementation and exhibits notable adaptability, promoting robust pre-training on evolving data. Comprehensive experiments across diverse downstream tasks compellingly demonstrate that RCP effectively alleviates the detrimental impact of concept drift, yielding more resilient and generalizable representations.

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