arXiv 2505.117962025-05-17cs.CV

CL-BioGAN：用于高光谱异常检测的受生物启发的跨域持续学习

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection

Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, School of Computer Science and Technology, Xidian University（协同智能系统重点实验室、教育部、计算机科学与技术学院、西安电子科技大学）；Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Artificial Intelligence, Xidian University（智能感知与图像理解重点实验室、教育部、人工智能学院、西安电子科技大学）；Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University（协同智能系统重点实验室、教育部、西安电子科技大学）

Jianing Wang, Zheng Hua, Wan Zhang, Shengjia Hao, Yuqiong Yao, Maoguo Gong

发表信息：: IEEE Transactions on Geoscience and Remote Sensing,2025
DOI：: 10.1109/TGRS.2025.3561174

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

针对跨场景高光谱异常检测中持续学习的稳定性与灵活性矛盾，提出受生物启发的CL-BioGAN，通过主动遗忘、CL损失与自注意力GAN平衡性能和成本。

摘要：

持续学习（CL）中的记忆稳定性与学习灵活性是跨场景高光谱异常检测（HAD）任务的核心挑战。生物神经网络能够通过调节由学习触发的突触扩张和突触收敛，主动遗忘与新经验学习相冲突的历史知识。受此现象启发，我们提出了一种新颖的受生物启发的持续学习生成对抗网络（CL-BioGAN），用于增强跨域HAD任务的连续分布拟合能力，其中融合了持续学习生物启发损失（CL-Bio Loss）和自注意力生成对抗网络（BioGAN），以实现遗忘历史知识，并在所提出的BioGAN中引入重放策略。具体而言，设计了一种由主动遗忘损失（AF Loss）和CL损失组成的新型生物启发损失，从贝叶斯视角实现新任务与历史任务之间的参数释放和增强。同时，带有L2范数的BioGAN损失增强了自注意力（SA），以进一步平衡稳定性和灵活性，从而为开放场景HAD（OHAD）任务更好地拟合背景分布。实验结果表明，所提出的CL-BioGAN能够以更少的参数和计算成本，为跨域HAD实现更稳健且令人满意的精度。这一双重贡献不仅提升了CL性能，也为OHAD任务中的神经适应机制提供了新的见解。

英文摘要：

Memory stability and learning flexibility in continual learning (CL) is a core challenge for cross-scene Hyperspectral Anomaly Detection (HAD) task. Biological neural networks can actively forget history knowledge that conflicts with the learning of new experiences by regulating learning-triggered synaptic expansion and synaptic convergence. Inspired by this phenomenon, we propose a novel Biologically-Inspired Continual Learning Generative Adversarial Network (CL-BioGAN) for augmenting continuous distribution fitting ability for cross-domain HAD task, where Continual Learning Bio-inspired Loss (CL-Bio Loss) and self-attention Generative Adversarial Network (BioGAN) are incorporated to realize forgetting history knowledge as well as involving replay strategy in the proposed BioGAN. Specifically, a novel Bio-Inspired Loss composed with an Active Forgetting Loss (AF Loss) and a CL loss is designed to realize parameters releasing and enhancing between new task and history tasks from a Bayesian perspective. Meanwhile, BioGAN loss with L2-Norm enhances self-attention (SA) to further balance the stability and flexibility for better fitting background distribution for open scenario HAD (OHAD) tasks. Experiment results underscore that the proposed CL-BioGAN can achieve more robust and satisfying accuracy for cross-domain HAD with fewer parameters and computation cost. This dual contribution not only elevates CL performance but also offers new insights into neural adaptation mechanisms in OHAD task.

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。