Construct · 1 fields · 1 citations

spectral normalization: a standalone construct

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Machine learning

dividing each layer's weight matrix by its largest singular value to control the Lipschitz constant of a GAN discriminator and stabilize training.

In machine learning, spectral normalization refers to dividing each layer's weight matrix by its largest singular value to control the Lipschitz constant of a GAN discriminator and stabilize training. This meaning comes from Miyato (2018) in arXiv.[1]

Unit
Recovery means
Measured by

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Nearby constructs

constant2 fields · 2 citationsjingle · one word, two constructs control4 fields · 5 citationsjingle · one word, four constructs layer3 fields · 5 citationsjingle · one word, three constructs

References

Miyato, T., Kataoka, T., Koyama, M., & Yoshida, Y. (2018). Spectral Normalization for Generative Adversarial Networks. arXiv. https://doi.org/10.48550/arxiv.1802.05957

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