TADIL: Task-Agnostic Domain-Incremental Learning through Task-ID Inference using Transformer Nearest-Centroid Embeddings

Climate BSC Group: Computer Sciences Software

TADIL (Task-Agnostic Domain-Incremental Learning) is a joint BSC/Lenovo effort in the autonomous driving domain that incorporates Continual Learning concepts to adaptively process data from diverse urban and rural driving environments. TADIL's main objective is to create a domain-incremental detector capable of handling various real-world scenarios like changing weather, time of the day, and locations.