Normalizing flows

Relative gradient optimization of the Jacobian term in unsupervised deep learning
By using relative gradients we propose a new approach for exact training of neural networks where the log-determinant of the Jacobian appears in the loss function as it happens, e.g., in normalizing flows.
Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvarinen
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Relative gradient optimization of the Jacobian term in unsupervised deep learning