IMPACT derives a closed-form low-rank activation reconstruction from an importance-weighted covariance matrix to achieve higher compression ratios than standard methods while maintaining model accuracy.
Exploiting linear structure within convolutional networks for efficient evaluation
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IMPACT: Importance-Aware Activation Space Reconstruction
IMPACT derives a closed-form low-rank activation reconstruction from an importance-weighted covariance matrix to achieve higher compression ratios than standard methods while maintaining model accuracy.