Papers › Towards Enabling Meta-Learning from Target Models

Towards Enabling Meta-Learning from Target Models

8 Apr 2021NeurIPS 2021 12arXiv:2104.03736archive 2025-07-28

Su Lu, Han-Jia Ye, Le Gan, De-Chuan Zhan

Meta-learning can extract an inductive bias from previous learning experience and assist the training of new tasks. It is often realized through optimizing a meta-model with the evaluation loss of task-specific solvers. Most existing algorithms sample non-overlapping 𝑠𝑢𝑝𝑝𝑜𝑟𝑡 sets and 𝑞𝑢𝑒𝑟𝑦 sets to train and evaluate the solvers respectively due to simplicity (𝒮/𝒬 protocol). Different from 𝒮/𝒬 protocol, we can also evaluate a task-specific solver by comparing it to a target model 𝒯, which is the optimal model for this task or a model that behaves well enough on this task (𝒮/𝒯 protocol). Although being short of research, 𝒮/𝒯 protocol has unique advantages such as offering more informative supervision, but it is computationally expensive. This paper looks into this special evaluation method and takes a step towards putting it into practice. We find that with a small ratio of tasks armed with target models, classic meta-learning algorithms can be improved a lot without consuming many resources. We empirically verify the effectiveness of 𝒮/𝒯 protocol in a typical application of meta-learning, 𝑖.𝑒., few-shot learning. In detail, after constructing target models by fine-tuning the pre-trained network on those hard tasks, we match the task-specific solvers and target models via knowledge distillation.

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