ACDP: Architecture-aware Cross-Dataset Performance Predictor for NAS

Performance predictors are pivotal in accelerating evaluation in NAS. Cross-dataset predictors are trained on (dataset, architecture, performance) triplets to efficiently estimate architectural performance for unseen datasets. However, existing cross-dataset predictors rely on a naive concatenation of dataset and architecture features. This fusion ignores the architecture-aware nature, where the same dataset manifests as distinct representations under different architectures, thereby limiting generalization. To tackle this issue, we propose ACDP, an Architecture-aware Cross-Dataset Performance Predictor that dynamically adjusts dataset feature extraction based on specific architecture. Initially, ACDP develops an elastic mechanism to extract raw dataset traits, providing a configurable lever for the trade-off between efficiency and accuracy. Furthermore, ACDP leverages a hypernetwork to determine dataset feature extractor parameters conditioned on architectural features, transforming raw dataset traits into tailored representations. ACDP excels in both specialization and generalization on six unseen datasets. Notably, in NAS-Bench-201, ACDP achieves 0.758/0.741 Kendall's Tau on CIFAR-10/100, surpassing single-dataset SOTAs trained with over 100 target samples. In MobileNetV3, ACDP discovers architectures outperforming cross-dataset SOTAs, notably gaining +2.04% accuracy on Aircraft. Code: https://anonymous.4open.science/r/ACDP/