The field of few-shot learning has recently seen substantial advancements. Most of these advancements came from casting few-shot learning as a meta-learning problem. Model Agnostic Meta Learning or MAML is currently one of the best approaches for few-shot learning via meta-learning. MAML is simple, elegant and very powerful, however, it has a variety of issues, such as being very sensitive to neural network architectures, often leading to instability during training, requiring arduous hyperparameter searches to stabilize training and achieve high generalization and being very computationally expensive at both training and inference times. In this paper, we propose various modifications to MAML that not only stabilize the system, but also substantially improve the generalization performance, convergence speed and computational overhead of MAML, which we call MAML++.

翻译：微小的学习领域最近取得了显著进步,其中多数进展来自将微小的学习作为一个元学习问题。模范名模学习或MAML是目前通过元学习进行微小学习的最佳方法之一。但MAML简单、优雅和非常有力,但是它有各种问题,例如对神经网络结构非常敏感,往往导致培训过程中的不稳定,需要艰苦的超光谱搜索,以稳定培训,实现高度概括化,而且在培训和推论期间都非常计算昂贵。在本文中,我们建议对MAML进行各种修改,不仅稳定系统,而且大幅度改进MAML(我们称之为MAML+)的一般性工作、趋同速度和计算间接费用。