What is recorded here?
Applied sparsity penalties to channel scaling factors, notably batch-normalization gamma, then removed low-scale channels and fine-tuned the narrower dense network.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits channel pruning, batch normalization and sparsity regularization. Training, pruning and fine-tuning remain necessary; the search changes layer widths rather than discovering arbitrary architectures. Residual/pre-activation networks require channel-selection handling. Smaller operation counts do not prescribe identical latency gains across platforms.
Read the original work
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Zhuang Liu; Jianguo Li; Zhiqiang Shen; Gao Huang; Shoumeng Yan; Changshui Zhang
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.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
Network slimming through sparse channel-scale learning
Applied sparsity penalties to channel scaling factors, notably batch-normalization gamma, then removed low-scale channels and fine-tuned the narrower dense network.
Scientific scope
Inherits channel pruning, batch normalization and sparsity regularization. Training, pruning and fine-tuning remain necessary; the search changes layer widths rather than discovering arbitrary architectures. Residual/pre-activation networks require channel-selection handling. Smaller operation counts do not prescribe identical latency gains across platforms.
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
Zhuang Liu; Jianguo Li; Zhiqiang Shen; Gao Huang; Shoumeng Yan; Changshui Zhang
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.