A Hypernetwork provides a mechanism for creating adaptable or dynamic neural network architectures. The generated parameters can be adjusted based on input data or specific tasks, allowing for more flexibility in model representation. Hypernetworks find applications in various areas, including meta-learning, reinforcement learning, and scenarios where model parameters need to be adjusted rapidly based on changing conditions.