用闭式解和加权损失改进MAML，提升少样本学习的稳定性和效率。
Fast Adaptation with Kernel and Gradient based Meta Leaning
- 内循环改用函数空间闭式解，避免多步梯度更新
- 外循环对任务损失加权，加速训练与推理收敛
- 为元学习提供新范式，适合快速适应场景
模型无关元学习（MAML）是少样本学习的标准元学习方法，虽通用但存在训练与推理阶段不稳定、计算低效的问题。本文提出两种算法：第一，在函数空间重新定义优化问题，以内循环使用闭式解替代多步梯度更新；第二，在外循环中为每个任务的损失分配权重，优化元学习器的更新过程。该方法在训练和推理阶段均实现更快收敛。实验表明，所提方法在理论和实践中均有显著突破，相比现有方法更高效地支持少样本学习与快速任务适配，为元学习建立新范式奠定基础。
原文摘要 · Abstract (English)
Model Agnostic Meta Learning or MAML has become the standard for few-shot learning as a meta-learning problem. MAML is simple and can be applied to any model, as its name suggests. However, it often suffers from instability and computational inefficiency during both training and inference times. In this paper, we propose two algorithms to improve both the inner and outer loops of MAML, then pose an important question about what 'meta' learning truly is. Our first algorithm redefines the optimization problem in the function space to update the model using closed-form solutions instead of optimizing parameters through multiple gradient steps in the inner loop. In the outer loop, the second algorithm adjusts the learning of the meta-learner by assigning weights to the losses from each task of the inner loop. This method optimizes convergence during both the training and inference stages of MAML. In conclusion, our algorithms offer a new perspective on meta-learning and make significant discoveries in both theory and experiments. This research suggests a more efficient approach to few-shot learning and fast task adaptation compared to existing methods. Furthermore, it lays the foundation for establishing a new paradigm in meta-learning.
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