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

The 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 CL generative adversarial network (CL-BioGAN) for augmenting continuous distribution fitting ability for cross-domain HAD task, where CL bioinspired 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 bioinspired 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 $L_{2}$ -norm enhances self-attention (SA) to further balance the stability and flexibility for better fitting background distribution for open-scenario HAD (OHAD) tasks. Experimental results underscore that the proposed CL-BioGAN can achieve more robust and satisfying accuracy for cross-domain HAD with fewer parameters and computational cost. This dual contribution not only elevates CL performance but also offers new insights into neural adaptation mechanisms in OHAD task.