@inproceedings{
han2024exploring,
title={Exploring Stable Meta-optimization Patterns via Differentiable Reinforcement Learning for Few-shot Classification},
author={Zheng Han and Xiaobin Zhu and Chun Yang and Hongyang Zhou and Jingyan Qin and Xu-Cheng Yin},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=mQcebyha6k}
}
Existing few-shot learning methods generally focus on designing exquisite structures of meta-learners for learning task-specific prior to improve the discriminative ability of global embeddings. However, they often ignore the importance of learning stability in meta-training, making it difficult to obtain a relatively optimal model. From this key observation, we propose an innovative generic differentiable Reinforcement Learning (RL) strategy for few-shot classification. It aims to explore stable meta-optimization patterns in meta-training by learning generalizable optimizations for producing task-adaptive embeddings. Accordingly, our differentiable RL strategy models the embedding procedure of feature transformation layers in meta-learner to optimize the gradient flow implicitly. Also, we propose a memory module to associate historical and current task states and actions for exploring inter-task similarity. Notably, our RL-based strategy can be easily extended to various backbones. In addition, we propose a novel task state encoder to encode task representation, which fully explores inner-task similarities between support set and query set. Extensive experiments verify that our approach can improve the performance of different backbones and achieve promising results against state-of-the-art methods in few-shot classification. Our code is available at an anonymous site: https://anonymous.4open.science/r/db8f0c012/.
Exploring Stable Meta-optimization Patterns via Differentiable Reinforcement Learning for Few-shot Classification
Zheng Han Xiaobin Zhu Chun Yang Hongyang Zhou Jingyan Qin Xu-Cheng Yin