A Deep Image Prior network trained with an inertial dynamics system converges to a zero-loss solution with an accelerated exponential rate in continuous time, and a discretized version achieves linear convergence with comparable recovery bounds.
, author Attouch, H
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Implicit Regularization of the Deep Inverse Prior Trained with Inertia
A Deep Image Prior network trained with an inertial dynamics system converges to a zero-loss solution with an accelerated exponential rate in continuous time, and a discretized version achieves linear convergence with comparable recovery bounds.