Heterogeneous Training

Heterogeneous training focuses on developing machine learning models capable of effectively learning from diverse and inconsistent datasets, addressing challenges like varying data distributions, label spaces, and annotation qualities. Current research emphasizes techniques such as multi-stage training, domain generalization, and the use of low-rank adapters or supernets to handle these inconsistencies, often within federated learning frameworks. This research area is crucial for advancing artificial general intelligence and enabling robust model development in real-world applications where data is inherently heterogeneous, such as medical image analysis and sound event detection.