Gradient Regularization Improves Accuracy of Discriminative Models
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Publication date: 2018
Schedae Informaticae, 2018, Volume 27, pp. 31 - 45
https://doi.org/10.4467/20838476SI.18.003.10408Authors
Gradient Regularization Improves Accuracy of Discriminative Models
Regularizing the gradient norm of the output of a neural network is a powerful technique, rediscovered several times. This paper presents evidence that gradient regularization can consistently improve classification accuracy on vision tasks, using modern deep neural networks, especially when the amount of training data is small. We introduce our regularizers as members of a broader class of Jacobian-based regularizers. We demonstrate empirically on real and synthetic data that the learning process leads to gradients controlled beyond the training points, and results in solutions that generalize well.
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Information: Schedae Informaticae, 2018, Volume 27, pp. 31 - 45
Article type: Original article
Titles:
Gradient Regularization Improves Accuracy of Discriminative Models
Gradient Regularization Improves Accuracy of Discriminative Models
Alfréd Rényi Institute of Mathematics, Hungarian Academy of Sciences
ELTE, Institute of Mathematics, Department of Computer Science Budapest, Hungary
Alfréd Rényi Institute of Mathematics, Hungarian Academy of Sciences
ELTE, Institute of Mathematics, Department of Computer Science Budapest, Hungary
Alfréd Rényi Institute of Mathematics, Hungarian Academy of Sciences
Published at: 2018
Article status: Open
Licence: CC BY-NC-ND
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