Fully Connected Layer

Fully connected layers, a fundamental component of many neural networks, aim to establish complex relationships between input and output data through dense connections between neurons. Current research focuses on improving their efficiency and interpretability, exploring techniques like sparse connections, optimized dropout strategies, and novel architectures such as deformable butterfly networks and binary MLPs to reduce computational cost and enhance performance. These advancements are significant because they address limitations in existing fully connected layers, leading to more efficient and robust models for various applications, including computer vision, signal processing, and medical diagnosis.