CL-CaGAN：用于跨域高光谱异常检测的胶囊差分对抗持续学习
CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection
AI总结：
针对跨域高光谱异常检测中先验不足与灾难性遗忘问题，提出CL-CaGAN，通过胶囊对抗背景建模、聚类重放、自蒸馏正则化和可微增强提升检测与持续学习性能。
摘要：
异常检测（AD）在高光谱图像（HSI）处理领域引起了显著关注，且大多数现有基于深度学习（DL）的算法通过当前场景下的特定训练过程，展现出检测异常样本的巨大潜力。然而，在开放场景的跨域检测中，有限的先验信息和灾难性遗忘问题给现有深度学习结构带来了关键挑战。为提高检测性能，本文提出一种新颖的基于持续学习的胶囊差分生成对抗网络（CL-CaGAN），以提升跨场景学习性能，从而促进基于深度学习的结构在高光谱异常检测（HAD）任务中的实际应用。首先，构建了一种结合对抗学习网络的改进胶囊结构，用于估计背景分布，以克服先验信息不足的问题。为缓解灾难性遗忘现象，本文融合了基于聚类的样本重放策略和设计的额外自蒸馏正则化，以在持续异常检测任务中整合历史知识与未来知识；同时，通过精心设计的具有持续学习（CL）策略的结构，保留了从先前检测场景到当前场景的判别学习能力。此外，实施可微增强以提升训练数据的生成性能。这进一步以更好的收敛性稳定了训练过程，并有效巩固了背景样本的重建能力。为验证所提出的CL-CaGAN的有效性，我们在若干真实高光谱图像上进行了实验，结果表明，所提出的CL-CaGAN在跨域场景下表现出更高的检测性能和缓解灾难性遗忘的持续学习能力。
英文摘要：
Anomaly detection (AD) has attracted remarkable attention in hyperspectral image (HSI) processing fields, and most existing deep learning (DL)-based algorithms indicate dramatic potential for detecting anomaly samples through specific training process under current scenario. However, the limited prior information and the catastrophic forgetting problem indicate crucial challenges for existing DL structure in open scenarios cross-domain detection. In order to improve the detection performance, a novel continual learning-based capsule differential generative adversarial network (CL-CaGAN) is proposed to elevate the cross-scenario learning performance for facilitating the real application of DL-based structure in hyperspectral AD (HAD) task. First, a modified capsule structure with adversarial learning network is constructed to estimate the background distribution for surmounting the deficiency of prior information. To mitigate the catastrophic forgetting phenomenon, clustering-based sample replay strategy and a designed extra self-distillation regularization are integrated for merging the history and future knowledge in continual AD task, while the discriminative learning ability from previous detection scenario to current scenario is retained by the elaborately designed structure with continual learning (CL) strategy. In addition, the differentiable enhancement is enforced to augment the generation performance of the training data. This further stabilizes the training process with better convergence and efficiently consolidates the reconstruction ability of background samples. To verify the effectiveness of our proposed CL-CaGAN, we conduct experiments on several real HSIs, and the results indicate that the proposed CL-CaGAN demonstrates higher detection performance and continuous learning capacity for mitigating the catastrophic forgetting under cross-domain scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
