In this work, we propose a novel meta-learning approach for few-shot classification, which learns transferable prior knowledge across tasks and directly produces network parameters for similar unseen tasks with training samples. Our approach, called LGM-Net, includes two key modules, namely, TargetNet and MetaNet. The TargetNet module is a neural network for solving a specific task and the MetaNet module aims at learning to generate functional weights for TargetNet by observing training samples. We also present an intertask normalization strategy for the training process to leverage common information shared across different tasks. The experimental results on Omniglot and miniImageNet datasets demonstrate that LGM-Net can effectively adapt to similar unseen tasks and achieve competitive performance, and the results on synthetic datasets show that transferable prior knowledge is learned by the MetaNet module via mapping training data to functional weights. LGM-Net enables fast learning and adaptation since no further tuning steps are required compared to other meta-learning approaches.

本文提出了一种新的元学习方法：LGM-Net，通过学习跨任务的可转移先验知识并直接产生相似未见过任务的网络参数来进行少样本分类，其中包含 TargetNet 模块和 MetaNet 模块，且介绍了一种跨任务整合策略以利用不同任务之间的公共信息，实验结果表明，LGM-Net 可以有效地适应相似未见的任务，具有比其他元学习方法更快的学习和适应速度。

LGM-Net: 学习生成匹配网络进行少样本学习