What is recorded here?
Systematically modernized a ResNet design with transformer-inspired training and architectural choices while retaining standard convolutional building blocks.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits residual/depthwise convolution, normalization and training recipes. Acquired contribution is the concrete controlled design study and resulting architecture, not proof that convolutions universally dominate transformers.
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People and systems
Zhuang Liu; Hanzi Mao; Chao-Yuan Wu; Christoph Feichtenhofer; Trevor Darrell; Saining Xie
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
ConvNeXt: modernized convolutional architecture design study
Systematically modernized a ResNet design with transformer-inspired training and architectural choices while retaining standard convolutional building blocks.
Scientific scope
Inherits residual/depthwise convolution, normalization and training recipes. Acquired contribution is the concrete controlled design study and resulting architecture, not proof that convolutions universally dominate transformers.
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; Hanzi Mao; Chao-Yuan Wu; Christoph Feichtenhofer; Trevor Darrell; Saining Xie
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.