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.
Caltech-ucsd birds 200
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1roles
dataset 1polarities
use dataset 1representative citing papers
citing papers explorer
-
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.