AWhat changed
Designed squeeze/expand Fire modules and channel/filter allocation strategies to reduce parameters while maintaining measured ImageNet classification performance.
Scientific scope
Designed squeeze/expand Fire modules and channel/filter allocation strategies to reduce parameters while maintaining measured ImageNet classification performance. Inherits convolutional networks and compression. Sub-megabyte claims additionally use model compression and are not raw full-precision architecture storage; parameter count alone does not imply lower end-to-end latency everywhere.
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 convolutional networks and compression. Sub-megabyte claims additionally use model compression and are not raw full-precision architecture storage; parameter count alone does not imply lower end-to-end latency everywhere.
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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