What is recorded here?
Systematically evaluated convolutional depth using repeated small3×3filters and a common architecture, demonstrating effective16–19weight-layer visual classifiers.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits CNNs and small-filter predecessors, not their invention.2014preprint/competition result precedes2015conference; classification/localization placements differ. Parameter comparisons for convolutional stacks do not imply small total VGG memory or universal optimal depth.
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People and systems
Karen Simonyan; Andrew Zisserman
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English catalogue narrative
VGG: small-filter stacks and systematic deep visual classifiers
Systematically evaluated convolutional depth using repeated small3×3filters and a common architecture, demonstrating effective16–19weight-layer visual classifiers.
Scientific scope
Inherits CNNs and small-filter predecessors, not their invention.2014preprint/competition result precedes2015conference; classification/localization placements differ. Parameter comparisons for convolutional stacks do not imply small total VGG memory or universal optimal depth.
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
Karen Simonyan; Andrew Zisserman
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.