Cross-Domain Knowledge Separation and Positive Transmission for Noisy Domain Incremental Learning

Domain Incremental Learning (DIL) aims to continuously adapt to new domains while retaining the knowledge of previous domains. Existing DIL methods are generally built upon an idealized assumption of clean supervision. However, real-world data streams are often affected by label noise, giving rise to the more challenging Noisy Domain Incremental Learning (N-DIL) scenario. Under this setting, not only is intra-domain knowledge acquisition hindered, but inter-domain knowledge conflicts are also exacerbated, which amplifies catastrophic forgetting. To address these challenges, we propose a novel Cross-Domain Knowledge Separation and Positive Transmission (ST-Prompt) framework. Specifically, to mitigate inter-domain knowledge conflicts, a Cross-domain Prompt Knowledge Contrastive Isolator is developed to enhance domain-wise knowledge separation, thereby mitigating the cross-domain knowledge interference during inference. Furthermore, to improve intra-domain knowledge acquisition, a Synergistic Knowledge Transmission scheme is introduced, which extracts reliable knowledge from previous domains to facilitate noisy data learning in the current domain. These two components are mutually reinforcing, jointly promoting effective cross-domain knowledge learning and utilization. Extensive experiments on diverse benchmarks demonstrate that ST-Prompt achieves state-of-the-art performance. Our code will be released.