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.
& Couillet, R.On the spectrum of random features maps of high dimensional datain International Conference on Machine Learning(2018), 3063–3071 (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.