Empirical analysis of sequential ResNet-18 training on Split CIFAR-100 finds stable recovery subspace dimensionality supporting the Stable Recovery Manifold hypothesis that forgotten knowledge remains compactly decodable.
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The Stable Recovery Manifold: Geometric Principles Governing Recoverability in Continual Learning
Empirical analysis of sequential ResNet-18 training on Split CIFAR-100 finds stable recovery subspace dimensionality supporting the Stable Recovery Manifold hypothesis that forgotten knowledge remains compactly decodable.