通过兼顾一致与差异信息，提升跨场景高光谱图像的知识迁移效果。
Agreement Disagreement Guided Knowledge Transfer for Cross-Scene Hyperspectral Imaging
- 设计双向机制，同时利用源与目标域的一致性和差异性特征。
- 引入梯度对齐与逻辑值归一化，缓解优化中的梯度冲突与主导问题。
- 适合处理异构高光谱场景下的迁移学习任务，尤其在数据分布差异大时表现优异。
知识迁移在跨场景高光谱成像（HSI）中至关重要。然而，现有方法常忽视共享参数优化过程中出现的梯度冲突与主导梯度问题。同时，多数方法仅依赖目标特征的有限共享子集，未能充分捕捉目标场景中丰富的多样模式。为此，我们提出一种一致-不一致引导的知识迁移框架（ADGKT），融合双重机制以增强跨场景迁移性能。一致组件包含GradVac，用于对齐梯度方向以缓解源域与目标域间的冲突；以及LogitNorm，调节逻辑值幅度，防止单一梯度源主导。不一致组件包括不一致约束（DiR）和集成策略，用于捕获多样化的预测目标特征，并减轻关键目标信息的丢失。大量实验表明，该方法在异构高光谱场景下实现了稳健且平衡的知识迁移，显著优于现有方法。
原文摘要 · Abstract (English)
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.
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