AWhat changed
Systematically modernized a ResNet design with transformer-inspired training and architectural choices while retaining standard convolutional building blocks.
Scientific scope
Systematically modernized a ResNet design with transformer-inspired training and architectural choices while retaining standard convolutional building blocks. 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.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. 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.
Contributors
- Source-listed authors and technical contributors · SCIENCE: 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.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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