# Agreement Disagreement Guided Knowledge Transfer for Cross-Scene Hyperspectral Imaging

arXiv: https://arxiv.org/abs/2512.08990 · HTML: https://mlsanity.com/paper/2512.08990/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 8 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning](https://mlsanity.com/paper/2012.09816/) (2020)
- [Mitigating Neural Network Overconfidence with Logit Normalization](https://mlsanity.com/paper/2205.09310/) (2022)
- [Knowledge Distillation by On-the-Fly Native Ensemble](https://mlsanity.com/paper/1806.04606/) (2018)
- [Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models](https://mlsanity.com/paper/2010.05874/) (2020)
- [Online Knowledge Distillation with Diverse Peers](https://mlsanity.com/paper/1912.00350/) (2019)
- [A Survey on Negative Transfer](https://mlsanity.com/paper/2009.00909/) (2020)
- [Agree to Disagree: Diversity through Disagreement for Better Transferability](https://mlsanity.com/paper/2202.04414/) (2022)
- [Independent Component Alignment for Multi-Task Learning](https://mlsanity.com/paper/2305.19000/) (2023)
- [Feature Fusion for Online Mutual Knowledge Distillation](https://mlsanity.com/paper/1904.09058/) (2019)
- [On the Versatile Uses of Partial Distance Correlation in Deep Learning](https://mlsanity.com/paper/2207.09684/) (2022)
- [Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification](https://mlsanity.com/paper/1904.05200/) (2019)
- [Cross-domain CNN for Hyperspectral Image Classification](https://mlsanity.com/paper/1802.00093/) (2018)
- [Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2204.03144/) (2022)

## Abstract (arXiv)

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.