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CoRe: Learning compact representation with prior-guided initialization
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AI
DOI：10.1016/j.patcog.2026.113040.png)
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Abstract
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• A prior-guided initialization is developed to accurately estimate compact representations and preserve information. • Reveals residual designs as CoRe module special cases, offering insight into residual efficacy. • Each component maps to CoRe method, making CoRe module traceable. • CoRe module adapts flexibly to diverse architectures and tasks. • CoRe can replace components without extra parameters or add with minimal overhead.
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