MetaDelta: A Meta-Learning System for Few-shot Image Classification

• URL: http://arxiv.org/abs/2102.10744v1
• Date: Mon, 22 Feb 2021 02:57:22 GMT
• Title: MetaDelta: A Meta-Learning System for Few-shot Image Classification
• Authors: Yudong Chen, Chaoyu Guan, Zhikun Wei, Xin Wang, Wenwu Zhu
• Abstract summary: We propose MetaDelta, a novel practical meta-learning system for the few-shot image classification. Each meta-learner in MetaDelta is composed of a unique pretrained encoder fine-tuned by batch training and parameter-free decoder used for prediction.
• Score: 71.06324527247423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Meta-learning aims at learning quickly on novel tasks with limited data by transferring generic experience learned from previous tasks. Naturally, few-shot learning has been one of the most popular applications for meta-learning. However, existing meta-learning algorithms rarely consider the time and resource efficiency or the generalization capacity for unknown datasets, which limits their applicability in real-world scenarios. In this paper, we propose MetaDelta, a novel practical meta-learning system for the few-shot image classification. MetaDelta consists of two core components: i) multiple meta-learners supervised by a central controller to ensure efficiency, and ii) a meta-ensemble module in charge of integrated inference and better generalization. In particular, each meta-learner in MetaDelta is composed of a unique pretrained encoder fine-tuned by batch training and parameter-free decoder used for prediction. MetaDelta ranks first in the final phase in the AAAI 2021 MetaDL Challenge\footnote{https://competitions.codalab.org/competitions/26638}, demonstrating the advantages of our proposed system. The codes are publicly available at https://github.com/Frozenmad/MetaDelta.

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