What is recorded here?
Ranked trained convolutional filters by absolute weight sums and removed corresponding feature maps and downstream kernels, producing smaller dense operations rather than isolated sparse weights.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Filter magnitude is a practical proxy, with layer sensitivity and pruning ratios empirically selected. Greedy multi-layer selection is not globally optimal and residual links impose structural constraints. Accuracy recovery and speed depend on retraining, architecture and implementation, not only nominal filter/FLOP reductions.
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People and systems
Hao Li; Asim Kadav; Igor Durdanovic; Hanan Samet; Hans Peter Graf
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Structured CNN filter pruning by weight magnitude
Ranked trained convolutional filters by absolute weight sums and removed corresponding feature maps and downstream kernels, producing smaller dense operations rather than isolated sparse weights.
Scientific scope
Filter magnitude is a practical proxy, with layer sensitivity and pruning ratios empirically selected. Greedy multi-layer selection is not globally optimal and residual links impose structural constraints. Accuracy recovery and speed depend on retraining, architecture and implementation, not only nominal filter/FLOP reductions.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Hao Li; Asim Kadav; Igor Durdanovic; Hanan Samet; Hans Peter Graf
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.