Heterogeneous Domain

Heterogeneous domain research addresses the challenges of building and applying machine learning models across datasets with differing characteristics, such as varying data distributions, labels, or input modalities. Current research focuses on developing algorithms and model architectures (e.g., equivariant transformers, Gaussian mixture models, federated learning frameworks) that can effectively transfer knowledge between these diverse domains, often leveraging techniques like contrastive learning, prototype learning, and domain adaptation. This work is crucial for improving the robustness and generalizability of machine learning models in real-world applications where data is rarely homogeneous, impacting fields ranging from medical image analysis to materials science and IoT security.