Complementary Learning

Complementary learning explores the synergistic combination of different learning paradigms or models to improve performance beyond what individual approaches can achieve. Current research focuses on leveraging the complementary strengths of various data sources (e.g., image and event data for depth estimation, magnitude and derivative spectra for hyperspectral image classification), learning methods (e.g., supervised and self-supervised learning for model failure detection), and even distinct neural network architectures (e.g., fast and slow learning systems inspired by the brain's hippocampus and neocortex). This approach addresses limitations of single-model systems, particularly in handling data scarcity, distributional shifts, and catastrophic forgetting, leading to improved accuracy and robustness in diverse applications such as medical diagnosis, autonomous driving, and continual learning.