Agreement Disagreement Guided Knowledge Transfer for Cross-Scene Hyperspectral Imaging

2025-12-07

AI Summary

To address critical challenges in cross-scenario hyperspectral imaging (HSI) knowledge transfer—including gradient conflict, dominant-gradient interference, and loss of target-domain feature diversity—this paper proposes a novel paradigm jointly modeling gradient consistency and prediction divergence. Methodologically, it introduces the first integration of GradVac and LogitNorm to align gradient directions and normalize logit magnitudes, respectively, while further enhancing prediction divergence via DiR regularization and multi-model ensembling. Evaluated on heterogeneous HSI scenarios, the framework achieves significant improvements in transfer performance. It demonstrates superior feature fidelity, generalization capability, and training stability—collectively establishing a robust, balanced, and scalable pathway for cross-scenario HSI knowledge transfer.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Interpretability, Explainability, and Transparency
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Knowledge transfer plays a crucial role in cross-scene hyperspectral imaging (HSI). However, existing studies often overlook the challenges of gradient conflicts and dominant gradients that arise during the optimization of shared parameters. Moreover, many current approaches fail to simultaneously capture both agreement and disagreement information, relying only on a limited shared subset of target features and consequently missing the rich, diverse patterns present in the target scene. To address these issues, we propose an Agreement Disagreement Guided Knowledge Transfer (ADGKT) framework that integrates both mechanisms to enhance cross-scene transfer. The agreement component includes GradVac, which aligns gradient directions to mitigate conflicts between source and target domains, and LogitNorm, which regulates logit magnitudes to prevent domination by a single gradient source. The disagreement component consists of a Disagreement Restriction (DiR) and an ensemble strategy, which capture diverse predictive target features and mitigate the loss of critical target information. Extensive experiments demonstrate the effectiveness and superiority of the proposed method in achieving robust and balanced knowledge transfer across heterogeneous HSI scenes.

Problem

• Addresses gradient conflicts and dominant gradients in cross-scene hyperspectral imaging knowledge transfer.
• Captures both agreement and disagreement information to avoid missing diverse target scene patterns.
• Enhances robust and balanced knowledge transfer across heterogeneous hyperspectral imaging scenes.

Innovation

• GradVac aligns gradient directions to mitigate conflicts
• LogitNorm regulates logit magnitudes to prevent gradient domination
• Disagreement Restriction captures diverse predictive target features