这篇论文提出了一种新的深度人工免疫网络，用结构化亲和力替代传统扁平向量，在MNIST等数据集上实现了不错的无回放增量学习效果，还分析了关键机制，挺有意思的。
该研究提出一种基于结构亲和力的深度人工免疫网络（Deep AIN），用于无回放的视觉类增量学习。模型将视觉B细胞形式化为结构化模板，包括平移模板亲和力、零归一化互相关（ZNCC）滤波器和特征图绑定配置文件。在sklearn digits、MNIST、Fashion-MNIST和KMNIST数据集上的实验表明，保留响应图至关重要。特征图Deep AIN在sklearn digits上达到0.939的最终平衡准确率，在Fashion-MNIST上达到0.814，在KMNIST上达到0.853。
Structured Affinity for Unsupervised Visual Class-Incremental Memory in Deep Artificial Immune Networks
Artificial immune networks (AINs) are naturally memory-forming systems, but conventional visual AINs often rely on flattened vector affinity that ignores spatial structure. This paper studies whether structured, gradient-free immune affinity can make Deep AINs viable as replay-free visual class-incremental representation-memory learners. Visual B-cells are formalized as structured templates, including shifted-template affinity, zero-normalized cross-correlation (ZNCC) filters, and feature-map binding profiles. A repertoire is treated both as memory and as a representation-inducing basis, while depth is obtained by passing binding-profile response maps to subsequent immune layers. The resulting Deep AIN exhibits adaptive latent coordinate reorganization: as new classes arrive, the binding-profile space evolves while retaining recoverable structure for earlier classes. Experiments on sklearn digits, MNIST, Fashion-MNIST, and KMNIST show that preserving response maps is critical. Scalar binding-profile variants underperform, whereas feature-map Deep AINs learn class-discriminative visual memory without replay, label-driven immune updates, or backpropagation through the immune layers. On sklearn digits, downstream probes fitted on the learned binding profiles reach 0.939 final balanced accuracy with logistic regression and 0.902 with 1-nearest-neighbour after all ten classes are encountered, with initial-class retention of 0.978. Adaptive layer-wise scale calibration further improves the two-layer feature-map Deep AIN to 0.978 balanced accuracy. With the same calibration rule, Fashion-MNIST reaches 0.814 and KMNIST reaches 0.853. These probes are external validation tools, not components of the AIN. The results identify structured affinity, response-map preservation, adaptive latent reorganization, and layer-wise scale calibration as key mechanisms for replay-free visual immune memory.