Modular Learning

Modular learning focuses on decomposing complex tasks into smaller, more manageable sub-tasks, mirroring human cognitive processes. Current research emphasizes developing flexible, modular architectures and algorithms, such as those employing hierarchical structures, mutual information maximization, and adaptive training strategies to improve efficiency and performance. This approach is proving valuable across diverse applications, including robotics, medical image analysis, and autonomous driving, by enabling faster training, enhanced generalization, and improved robustness to noisy or incomplete data. The resulting modular systems offer increased interpretability and facilitate the reuse of learned components across different tasks.