Learning Entropy Signatures are formed from the K largest locations in Spatial Learning Entropy Maps generated via sequential MLP learning on image pixel neighborhoods and shown to retain discriminative power for classification.
Harris and Mike Stephens
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Spatial Learning Entropy Maps derived from MLP weight adaptations during spatial pixel prediction tasks highlight image points with high learning impact.
Particle Diffusion Matching uses diffusion-guided random walk searches to align challenging standard and ultra-widefield retinal images, claiming state-of-the-art benchmark performance.
citing papers explorer
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Learning Entropy Signature for Image Representation and Classification
Learning Entropy Signatures are formed from the K largest locations in Spatial Learning Entropy Maps generated via sequential MLP learning on image pixel neighborhoods and shown to retain discriminative power for classification.
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Particle Diffusion Matching: Random Walk Correspondence Search for the Alignment of Standard and Ultra-Widefield Fundus Images
Particle Diffusion Matching uses diffusion-guided random walk searches to align challenging standard and ultra-widefield retinal images, claiming state-of-the-art benchmark performance.