The proliferation of Few-Shot Class Incremental Learning (FSCIL) methodologies has highlighted the critical challenge of maintaining robust anti-amnesia capabilities in FSCIL learners. In this paper, we present a novel conceptualization of anti-amnesia in terms of mathematical generalization, leveraging the Neural Tangent Kernel (NTK) perspective. Our method focuses on two key aspects: ensuring optimal NTK convergence and minimizing NTK-related generalization loss, which serve as the theoretical foundation for cross-task generalization. To achieve global NTK convergence, we introduce a principled meta-learning mechanism that guides optimization within an expanded network architecture. Concurrently, to reduce the NTK-related generalization loss, we systematically optimize its constituent factors. Specifically, we initiate self-supervised pre-training on the base session to enhance NTK-related generalization potential. These self-supervised weights are then carefully refined through curricular alignment, followed by the application of dual NTK regularization tailored specifically for both convolutional and linear layers. Through the combined effects of these measures, our network acquires robust NTK properties, ensuring optimal convergence and stability of the NTK matrix and minimizing the NTK-related generalization loss, significantly enhancing its theoretical generalization. On popular FSCIL benchmark datasets, our NTK-FSCIL surpasses contemporary state-of-the-art approaches, elevating end-session accuracy by 2.9% to 9.3%.

中文翻译：

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NTK 指导的 Few-Shot Class 增量学习


Few-Shot Class Incremental Learning （FSCIL） 方法的普及凸显了在 FSCIL 学习者中保持强大的抗健忘症能力的关键挑战。在本文中，我们利用神经切线核 （NTK） 视角，从数学泛化的角度提出了一种新颖的抗健忘症概念化。我们的方法侧重于两个关键方面：确保最佳 NTK 收敛和最小化 NTK 相关的泛化损失，这是跨任务泛化的理论基础。为了实现全局 NTK 收敛，我们引入了一种原则化的元学习机制，该机制可在扩展的网络架构中指导优化。同时，为了减少 NTK 相关的泛化损失，我们系统地优化了其组成因子。具体来说，我们在基础会话上启动自我监督的预训练，以增强与 NTK 相关的泛化潜力。然后通过课程调整仔细细化这些自我监督的权重，然后应用专为卷积层和线性层量身定制的双 NTK 正则化。通过这些措施的综合作用，我们的网络获得了强大的 NTK 特性，确保了 NTK 矩阵的最佳收敛性和稳定性，并最大限度地减少了与 NTK 相关的泛化损失，显着增强了其理论泛化。在流行的 FSCIL 基准数据集上，我们的 NTK-FSCIL 超越了当代最先进的方法，将会话结束准确率提高了 2.9% 至 9.3%。