We propose a new technique, Singular Vector Canonical Correlation Analysis (SVCCA), a tool for quickly comparing two representations in a way that is both invariant to affine transform (allowing comparison between different layers and networks) and fast to compute (allowing more comparisons to be calculated than with p…
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3 results for “SVCCA”
Neural networks learn general representations for solving differential equations.
problem Solving parametrized boundary value problems using neural networks.
method Singular Vector Canonical Correlation Analysis (SVCCA) for measuring generality.
result First hidden layer learns general representations, deeper layers become more specific.
New method compares neural network representations, revealing generalization and structure.
problem Comparing neural network representations is hard due to varying structures and evolution.
method Projection Weighted CCA (CCA) to differentiate signal and noise.
result Networks that generalize converge to similar representations, while memorizers diverge.