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Found 3 papers, 1 papers with code
Frosting Weights for Better Continual Training
Training a neural network model can be a lifelong learning process and is a computationally intensive one.
On Computation and Generalization of Generative Adversarial Networks under Spectrum Control
Generative Adversarial Networks (GANs), though powerful, is hard to train.
On Computation and Generalization of GANs with Spectrum Control
Specifically, we propose a new reparameterization approach for the weight matrices of the discriminator in GANs, which allows us to directly manipulate the spectra of the weight matrices through various regularizers and constraints, without intensively computing singular value decompositions.
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