Tensor networks enable tunable, objective compression of 1D fluid data with lossless reconstruction at high bond dimension and efficient in-compressed-space operations like periodic convolution.
Deep learning-enabled detection and localization of gastrointestinal diseases in endoscopic imagery
2 Pith papers cite this work. Polarity classification is still indexing.
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A teacher-student knowledge distillation framework combining Swin Transformer and Vision Transformer reaches 99.78% and 99.28% accuracy with AUC 1.0 on two GI endoscopy datasets and uses Grad-CAM for interpretability.
citing papers explorer
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Tensor network compression using fluid dynamics as a testbed: Analytical foundations in one dimension
Tensor networks enable tunable, objective compression of 1D fluid data with lossless reconstruction at high bond dimension and efficient in-compressed-space operations like periodic convolution.
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A Graph-Augmented knowledge Distillation based Dual-Stream Vision Transformer with Region-Aware Attention for Gastrointestinal Disease Classification with Explainable AI
A teacher-student knowledge distillation framework combining Swin Transformer and Vision Transformer reaches 99.78% and 99.28% accuracy with AUC 1.0 on two GI endoscopy datasets and uses Grad-CAM for interpretability.