  Multi-domain learning (MDL) aims to train a model with minimal average risk
across multiple overlapping but non-identical domains. To tackle the challenges
of dataset bias and domain domination, numerous MDL approaches have been
proposed from the perspectives of seeking commonalities by aligning
distributions to reduce domain gap or reserving differences by implementing
domain-specific towers, gates, and even experts. MDL models are becoming more
and more complex with sophisticated network architectures or loss functions,
introducing extra parameters and enlarging computation costs. In this paper, we
propose a frustratingly easy and hyperparameter-free multi-domain learning
method named Decoupled Training (D-Train). D-Train is a tri-phase
general-to-specific training strategy that first pre-trains on all domains to
warm up a root model, then post-trains on each domain by splitting into
multi-heads, and finally fine-tunes the heads by fixing the backbone, enabling
decouple training to achieve domain independence. Despite its extraordinary
simplicity and efficiency, D-Train performs remarkably well in extensive
evaluations of various datasets from standard benchmarks to applications of
satellite imagery and recommender systems.
