模仿大脑类比学习机制，用混合原型提升小样本增量学习性能
Brain-inspired analogical mixture prototypes for few-shot class-incremental learning
- 用混合原型表示类别，结合统计类比与软投票策略
- 在小样本增量设置下准确率超越现有方法，显著缓解遗忘问题
- 适合关注神经科学启发模型、小样本学习的研究者
少样本增量学习（FSCIL）对人工神经网络提出严峻挑战，需在数据有限情况下高效学习并保留旧知识。受大脑分类与类比学习机制启发，本文提出脑启发式类比混合原型（BAMP）。BAMP包含三部分：混合原型特征学习、统计类比和软投票。基于预训练视觉变换器（ViT），混合原型特征学习在基础阶段使用原型混合表示每类，并微调这些表示。统计类比根据新类与基类的相似性校准原型均值与协方差矩阵，使用马氏距离计算分类得分。软投票融合统计类比与现成少样本增量学习方法的优势。在基准数据集上的实验表明，BAMP在传统大起点与挑战性小起点设置下均优于当前最优方法。研究显示，脑启发的类比混合原型可有效缓解灾难性遗忘与过拟合问题。
原文摘要 · Abstract (English)
Few-shot class-incremental learning (FSCIL) poses significant challenges for artificial neural networks due to the need to efficiently learn from limited data while retaining knowledge of previously learned tasks. Inspired by the brain's mechanisms for categorization and analogical learning, we propose a novel approach called Brain-inspired Analogical Mixture Prototypes (BAMP). BAMP has three components: mixed prototypical feature learning, statistical analogy, and soft voting. Starting from a pre-trained Vision Transformer (ViT), mixed prototypical feature learning represents each class using a mixture of prototypes and fine-tunes these representations during the base session. The statistical analogy calibrates the mean and covariance matrix of prototypes for new classes according to similarity to the base classes, and computes classification score with Mahalanobis distance. Soft voting combines both merits of statistical analogy and an off-shelf FSCIL method. Our experiments on benchmark datasets demonstrate that BAMP outperforms state-of-the-art on both traditional big start FSCIL setting and challenging small start FSCIL setting. The study suggests that brain-inspired analogical mixture prototypes can alleviate catastrophic forgetting and over-fitting problems in FSCIL.
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