Linear generative models memorize at small data loads but converge continuously once samples scale linearly with dimension; this convergence is insensitive to sharp recovery of principal latent factors.
& Bach, F.Implicit bias of gradient descent for wide two-layer neural networks trained with the logistic lossinConference on learning theory(2020), 1305–1338 (cit
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Memorisation, convergence and generalisation in generative models
Linear generative models memorize at small data loads but converge continuously once samples scale linearly with dimension; this convergence is insensitive to sharp recovery of principal latent factors.