CRAD-HOPE: brain-inspired nested learning framework for few-shot anomaly detection

Unsupervised anomaly detection has achieved progress with continuous memory representations, which store normal patterns in learnable grids via differentiable interpolation. However, existing methods (e.g., CRAD) employ static update rates, leading to overfitting and poor generalization in few-shot scenarios where available normal samples are scarce. To address this, we propose CRAD-HOPE, a brain-inspired nested learning framework. Our motivation is to address the stability-plasticity dilemma: effectively adapting to new few-shot samples without forgetting the general manifold of normality. Inspired by the brain’s Complementary Learning Systems (CLS), we design a Multi-Frequency Nested Grid architecture. It consists of a “Fast Grid” (simulating the hippocampus) for rapid adaptation to new patterns and a “Slow Grid” (simulating the neocortex) for stable long-term retention. A Grid Knowledge Distillation mechanism further consolidates information from fast to slow grids to help prevent catastrophic forgetting. Extensive experiments on MVTec AD and OCT2017 demonstrate that CRAD-HOPE achieves state-of-the-art performance, improving 1-shot detection by 4.1% I-AUROC over the baseline and achieving superior pixel-level localization (94.7% P-AUROC) with 9.5$$\times $$ fewer parameters than VLM-based approaches. Code is available at https://github.com/tiankongkongkongruye/CRAD_HOPE.