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Implicit Gradient Regularization
[article]
2022
arXiv
pre-print
Gradient descent can be surprisingly good at optimizing deep neural networks without overfitting and without explicit regularization. We find that the discrete steps of gradient descent implicitly regularize models by penalizing gradient descent trajectories that have large loss gradients. We call this Implicit Gradient Regularization (IGR) and we use backward error analysis to calculate the size of this regularization. We confirm empirically that implicit gradient regularization biases
arXiv:2009.11162v3
fatcat:mdg3waocyjg4jo2xbvori4mlwi