REMIX uses Laplace kernel parameterization to enable scalable full-covariance modeling in model inversion, improving synthetic sample quality and performance in data-free continual learning.
15 At the topmost layer ( l=L ), the optimization aims to align the generated features directly with the target class label y
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Stop Marginalizing My Dreams: Model Inversion via Laplace Kernel for Continual Learning
REMIX uses Laplace kernel parameterization to enable scalable full-covariance modeling in model inversion, improving synthetic sample quality and performance in data-free continual learning.