If a neural network's output mapping consistently reduces the input space distance during training, which consequence follows regarding convergence?

💡 Explanation

Convergence to a fixed point is guaranteed because the Banach fixed-point theorem ensures that a contraction mapping has a unique fixed point. Therefore, the network will converge, rather than oscillate or diverge, provided the mapping remains contractive.

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