Multi-Domain Learning with Global Expert Mapping

2026-04-20

AI Summary

This work addresses the challenges of weak generalization, expert redundancy, and degraded performance on rare domains in multi-dataset joint training, which stem from distribution shifts and semantic inconsistencies in labels. To this end, the authors propose the GEM framework, which replaces the learnable router in conventional Mixture-of-Experts (MoE) architectures with a planner–compiler design. GEM introduces a deterministic expert assignment mechanism based on linear programming relaxation and hierarchical rounding, strictly enforcing capacity constraints without relying on load-balancing losses. This approach significantly enhances expert specialization and routing interpretability. Integrated with a DINO vision backbone, GEM-DINO achieves state-of-the-art performance on the UODB benchmark, markedly improving results on rare datasets and effectively mitigating task interference in few-shot adaptation scenarios.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Human perception generalizes well across different domains, but most vision models struggle beyond their training data. This gap motivates multi-dataset learning, where a single model is trained on diverse datasets to improve robustness under domain shifts. However, unified training remains challenging due to inconsistencies in data distributions and label semantics. Mixture-of-Experts (MoE) models provide a scalable solution by routing inputs to specialized subnetworks (experts). Yet, existing MoEs often fail to specialize effectively, as their load-balancing mechanisms enforce uniform input distribution across experts. This fairness conflicts with domain-aware routing, causing experts to learn redundant representations, and reducing performance especially on rare or out-of-distribution domains. We propose GEM (Global Expert Mapping), a planner-compiler framework that replaces the learned router with a global scheduler. Our planner, based on linear programming relaxation, computes a fractional assignment of datasets to experts, while the compiler applies hierarchical rounding to convert this soft plan into a deterministic, capacity-aware mapping. Unlike prior MoEs, GEM avoids balancing loss, resolves the conflict between fairness and specialization, and produces interpretable routing. Experiments show that GEM-DINO achieves state-of-the-art performance on the UODB benchmark, with notable gains on underrepresented datasets and solves task interference in few-shot adaptation scenarios.

Problem

• Multi-Domain Learning
• Mixture-of-Experts
• Domain Shift
• Expert Specialization
• Load Balancing

Innovation

• Global Expert Mapping
• Mixture-of-Experts
• multi-domain learning
• linear programming relaxation
• hierarchical rounding