LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
This addresses the problem of fast adaptation in few-shot learning for AI applications, though it appears incremental as it builds on existing meta-learning methods.
The paper tackles few-shot classification by proposing LGM-Net, a meta-learning approach that learns to generate network parameters for unseen tasks, achieving competitive performance on Omniglot and miniImageNet datasets.
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.