Abstract
Although large scale models achieve significant improvements in performance, the overfitting challenge still frequently undermines their generalization ability. In super resolution tasks on images, diffusion models as representatives of generative models typically adopt large scale architectures. However, few-shot drone-captured infrared training data frequently induces severe overfitting in large-scale architectures. To address this key challenge, our method proposes a new Gaussian quantization representation learning method oriented to diffusion models that alleviates overfitting and enhances robustness. At the same time, an effective monitoring mechanism tracks large scale architectures during training to detect signs of overfitting. By introducing Gaussian quantization representation learning, our method effectively reduces overfitting while maintaining architecture complexity. On this basis, we construct a multi source drone-based infrared image benchmark dataset for detection and use it to emphasize overfitting issues of large scale architectures in few sample, drone-based diverse drone-based image reconstruction scenarios. To verify the efficacy of the method in mitigating overfitting, experiments are conducted on the constructed benchmark. Experimental results demonstrate that our method outperforms existing super resolution approaches and significantly mitigates overfitting of large scale architectures under complex conditions. The code and DroneSR dataset will be available at: this https URL.
Abstract (translated)
尽管大规模模型在性能上取得了显著的改进，但过拟合的问题依然频繁地损害了它们的泛化能力。特别是在图像超分辨率任务中，作为生成式模型代表的扩散模型通常采用大规模架构。然而，在使用少量无人机捕捉到的红外训练数据的情况下，这类大型架构经常会引发严重的过拟合问题。为解决这一关键挑战，我们的方法提出了一种针对扩散模型的新型高斯量化表征学习方法，以减轻过拟合并增强鲁棒性。同时，我们设计了一个有效的监控机制，在训练过程中追踪大规模架构，以便及时发现过拟合的迹象。通过引入高斯量化表征学习，我们的方法在不增加架构复杂度的情况下有效减少了过拟合现象。 在此基础上，我们构建了一个基于多源无人机红外图像的数据集（用于检测），并在该数据集中突出展示了大规模架构在少样本、多样化无人机捕捉场景中的重建任务中所面临的过拟合问题。为了验证该方法缓解过拟合的有效性，在构造的基准测试上进行了实验。实验结果表明，我们的方法优于现有的超分辨率方法，并显著减轻了复杂条件下的大规模模型的过拟合现象。 相关代码和DroneSR数据集可在以下链接获取：[提供具体的网址]
URL
https://arxiv.org/abs/2509.01898
