Meta-BN Net for few-shot learning

doi:10.1007/s11704-021-1237-4

Frontiers of Computer Science

2023, Vol. 17

Issue (1): 171302 https://doi.org/10.1007/s11704-021-1237-4

本期目录

Meta-BN Net for few-shot learning

Wei GAO¹, Mingwen SHAO¹(

), Jun SHU², Xinkai ZHUANG¹

¹. School of Computer Science, China University of Petroleum, Qingdao 266580, China
². School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China

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Abstract：

In this paper, we propose a lightweight network with an adaptive batch normalization module, called Meta-BN Net, for few-shot classification. Unlike existing few-shot learning methods, which consist of complex models or algorithms, our approach extends batch normalization, an essential part of current deep neural network training, whose potential has not been fully explored. In particular, a meta-module is introduced to learn to generate more powerful affine transformation parameters, known as $γ$ and $β$ , in the batch normalization layer adaptively so that the representation ability of batch normalization can be activated. The experimental results on miniImageNet demonstrate that Meta-BN Net not only outperforms the baseline methods at a large margin but also is competitive with recent state-of-the-art few-shot learning methods. We also conduct experiments on Fewshot-CIFAR100 and CUB datasets, and the results show that our approach is effective to boost the performance of weak baseline networks. We believe our findings can motivate to explore the undiscovered capacity of base components in a neural network as well as more efficient few-shot learning methods.

Key words： meta-learning few-shot learning batch normalization

收稿日期: 2021-05-07 出版日期: 2022-03-01

Corresponding Author(s): Mingwen SHAO

引用本文:

. [J]. Frontiers of Computer Science, 2023, 17(1): 171302.
Wei GAO, Mingwen SHAO, Jun SHU, Xinkai ZHUANG. Meta-BN Net for few-shot learning. Front. Comput. Sci., 2023, 17(1): 171302.

链接本文:

https://academic.hep.com.cn/fcs/CN/10.1007/s11704-021-1237-4
https://academic.hep.com.cn/fcs/CN/Y2023/V17/I1/171302

Fig.1

Fig.2

Tab.1

$γ$ & $β$	5-way 1-shot	5-way 5-shot	DB index
Naive	33.75±0.17	63.23±0.17	0.31
Fixed	40.29±0.18	63.18±0.18	0.26
Meta-BN	71.73±0.23	82.58±0.17	0.04

Tab.2

$γ$ & $β$	5-way 1-shot	5-way 5-shot	DB index
Naive	36.73±0.17	63.98±0.17	0.24
Fixed	38.64±0.17	63.36±0.18	0.25
Meta-BN	40.89±0.18	68.57±0.17	0.21

Tab.3

$γ$ & $β$	5-way 1-shot	5-way 5-shot	DB index
Naive	29.56±0.14	48.18±0.18	0.25
Fixed	31.50±0.15	48.92±0.17	0.22
Meta-BN	32.29±0.15	50.49±0.18	0.19

Tab.4

Fig.3

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