OmniISR unifies centralized, federated, and hybrid learning by injecting mutual-information supervision and negative-entropy regularization at multiple hidden layers, with supporting convergence and drift bounds.
A comprehensive review on deep supervision: Theories and applications
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Evaluating DPSGD clipping methods on medical segmentation shows prior assumptions fail in this domain, but adding morphological refinement and an adaptive DP-Morph variant improves utility under privacy constraints.
citing papers explorer
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OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization
OmniISR unifies centralized, federated, and hybrid learning by injecting mutual-information supervision and negative-entropy regularization at multiple hidden layers, with supporting convergence and drift bounds.
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From Gradient Clipping to Structural Refinement: Improving DPSGD for Medical Image Segmentation
Evaluating DPSGD clipping methods on medical segmentation shows prior assumptions fail in this domain, but adding morphological refinement and an adaptive DP-Morph variant improves utility under privacy constraints.