AWhat changed
Passed encoder max-pooling locations to the decoder for sparse unpooling, then used trainable convolutions to densify features before pixel classification, studying memory–accuracy tradeoffs.
Scientific scope
Passed encoder max-pooling locations to the decoder for sparse unpooling, then used trainable convolutions to densify features before pixel classification, studying memory–accuracy tradeoffs. Pooling-index unpooling inherits earlier unsupervised and visualization methods; the contribution is its trained segmentation architecture and evaluation. No learned unpooling does not mean the decoder has no learned weights. Storing indices trades memory against information and accuracy.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Pooling-index unpooling inherits earlier unsupervised and visualization methods; the contribution is its trained segmentation architecture and evaluation. No learned unpooling does not mean the decoder has no learned weights. Storing indices trades memory against information and accuracy.
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.
Full scientific record · Browse contribution grades · Request a correction