Residual networks admit progressive approximation trajectories with monotonically decreasing error, enabling useful predictions from any depth after a single training run via the LPA principle.
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Progressive Approximation in Deep Residual Networks: Theory and Validation
Residual networks admit progressive approximation trajectories with monotonically decreasing error, enabling useful predictions from any depth after a single training run via the LPA principle.