Wada architecture

The Wada architecture is a design used in neural networks to improve learning efficiency. It splits the network’s processing into two parts: one handles new, primary tasks, while the other maintains past knowledge. By doing so, it helps the network adapt to new information without losing what it has already learned, reducing problems like forgetting previous tasks. Think of it as a way to keep previous knowledge safe while learning new skills, enabling more stable and flexible performance across multiple tasks.