Abstract: Highlights•An efficient Riemannian meta-optimization method for large-scale optimization.•A subspace adaptation scheme for parameter sharing in Riemannian meta-optimization.•Reduce memory consumption of optimizing neural networks by six orders of magnitude.
External IDs:doi:10.1016/j.cviu.2025.104306
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