Few-Shot Learning (FSL) is a challenging task, i.e., how to recognize novel classes with few examples? Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then predict novel classes via a nearest neighbor classifier with mean-based prototypes. Nevertheless, due to the data scarcity, the mean-based prototypes are usually biased. In this paper, we diminish the bias by regarding it as a prototype optimization problem. Although the existing meta-optimizers can also be applied for the optimization, they all overlook a crucial gradient bias issue, i.e., the mean-based gradient estimation is also biased on scarce data. Consequently, we regard the gradient itself as meta-knowledge and then propose a novel prototype optimization-based meta-learning framework, called MetaNODE. Specifically, we first regard the mean-based prototypes as initial prototypes, and then model the process of prototype optimization as continuous-time dynamics specified by a Neural Ordinary Differential Equation (Neural ODE). A gradient flow inference network is carefully designed to learn to estimate the continuous gradients for prototype dynamics. Finally, the optimal prototypes can be obtained by solving the Neural ODE using the Runge-Kutta method. Extensive experiments demonstrate that our proposed method obtains superior performance over the previous state-of-the-art methods. Our code will be publicly available upon acceptance.

本研究提出了一种基于元学习的原型优化框架，称为MetaNODE，通过神经常微分方程模型优化模型原型，解决了在稀疏数据上梯度信息失真的问题，从而实现了有效的Few-Shot Learning。在miniImagenet、tieredImagenet和CUB-200-2011等数据集上进行了广泛实验。

MetaNODE: 面向少样本学习的神经ODE原型优化