DAMEL: Dual-Axis Multi-Expert Learning for Class-Imbalanced Learning
DAMEL proposes a new method to improve class-imbalanced learning by using multiple experts along two axes: representation and time. It combines expert representations via concatenation and aggregates network weights over epochs using exponential moving averages (EMA). This dual-axis ensemble reduces both prediction bias and variance, leading to higher accuracy on various challenging imbalanced datasets.