All-in-One Robust Model for Dynamic Environments

Real-world machine learning systems are increasingly deployed in diverse and dynamic environments, where a model must handle a range of operating conditions and adapt to new ones as they arise. The standard solution, training and maintaining a separate specialist model for each condition, becomes increasingly costly as the number of conditions grows and new conditions continue to appear. We propose the Condition-Aware Network (CAN), which consists of a condition-invariant shared backbone and a lightweight conditional adaptor, allowing a single model to switch flexibly between operating conditions. CAN is trained with a two-stage recipe: joint training over a spectrum of conditions to build a shared representation, followed by fast adaptation that fine-tunes only the lightweight adaptor to a new condition without retraining the backbone. We instantiate CAN primarily in the context of adversarial image classification, and further validate the same conditioning mechanism on image restoration and adversarial text classification, showing that it generalizes across domains with structurally different notions of operating condition. Extensive experiments show that CAN substantially reduces the resources needed to cover multiple conditions while maintaining robustness comparable to a collection of independently trained specialist models. In the CIFAR-10 experiment with a ResNet-18 backbone, CAN reduces training GPU hours by 87.5% and stored parameters by 88.0%, while nearly matching the specialists' robust accuracy. Our code is available at https://anonymous.4open.science/r/CAN-CF44/.