ADRformer
ADRformer is a transformer-based neural network architecture proposed for robust cross-domain learning. It aims to maintain high performance when models are applied to data distributions that differ from those seen during training by integrating adaptive mechanisms into the standard transformer framework.
The architecture adds domain-aware adapters and Adaptive Domain Regularization (ADR) modules that adjust attention and feed-forward
ADRformer is intended for tasks including natural language processing, time-series forecasting, and multimodal classification across multiple
As a concept in research literature, ADRformer is discussed as part of ongoing work on efficient domain
Related topics include transformers, adapters, and domain adaptation.