训练过程中使用支持集信息的单样本学习算法

doi:10.3969/j.issn.0253-2778.2020.08.020

中国科学技术大学学报 ›› 2020, Vol. 50 ›› Issue (8): 1187-1192.DOI: 10.3969/j.issn.0253-2778.2020.08.020

• 论著 • 上一篇

训练过程中使用支持集信息的单样本学习算法

辛守宇，郑蕊蕊，周瑜，刘文鹏，贺建军

大连民族大学信息与通信工程学院，辽宁大连 116000

收稿日期:2020-06-03 修回日期:2020-07-09 接受日期:2020-07-09 出版日期:2020-08-31 发布日期:2020-07-09
通讯作者: 贺建军
作者简介:辛守宇，男，1995年生，硕士研究生.研究方向：深度学习.Email：xsy2268745113@163.com
基金资助:
国家自然科学基金（61702081）资助.

A one-shot learning algorithm using support set information during training

XIN Shouyu, ZHENG Ruirui, ZHOU Yu, LIU Wenpeng, HE Jianjun

College of Information and Communication Engineering, Dalian Minzu University, Dalian 116000, China

Received:2020-06-03 Revised:2020-07-09 Accepted:2020-07-09 Online:2020-08-31 Published:2020-07-09

摘要/Abstract

摘要： 单样本学习的目的是利用一个包含大量训练样本的源类别数据集以及每个类别只包含一个训练样本的目标类别数据集来构建一种学习算法，使得算法能够对目标类别空间中的样本进行准确分类.已有的单样本学习算法主要是先利用源类别数据来训练模型，然后在测试时将目标类别训练数据作为支持集来实现对未标注样本的分类，因此在训练时没有有效地利用支持集的信息.为此提出一种在训练阶段和测试阶段同时利用支持集信息的单样本学习算法，基本思想是利用孪生神经网络构建模型并在训练时加入支持集信息，即让不同类别的支持集样本之间的相似度尽可能小.在Omniglot数据集和满文识别问题上的实验结果表明，该算法能取得较好的识别准确率.

关键词: 单样本学习, 孪生神经网络, 度量学习

Abstract: The purpose of one-shot learning is to use a source category dataset containing a large number of training samples and a target category dataset containing only one training sample per category to construct a learning algorithm that enables accurate classification of samples in the target category space. The existing one-shot learning algorithm mainly uses the source category data to train the model, and then uses the training data of the target category as the support set to realize the classification of the unlabeled samples during the test. Therefore, it fails to effectively utilize the information of the support set during the training. Here, a one-shot learning algorithm using support set information in both the training and test stages is established. The basic idea is to use Siamese neural networks to build models and add support set information during training, that is, to make the similarity between different types of support set samples as small as possible. Experimental results on Omniglot data set and Manchu recognition show that the proposed algorithm can achieve better recognition accuracy.

Key words: one-shot learning, siamese neural networks, metric learning

中图分类号:

TP391.1

辛守宇，郑蕊蕊，周瑜，刘文鹏，贺建军. 训练过程中使用支持集信息的单样本学习算法[J]. 中国科学技术大学学报, 2020, 50(8): 1187-1192.

XIN Shouyu, ZHENG Ruirui, ZHOU Yu, LIU Wenpeng, HE Jianjun. A one-shot learning algorithm using support set information during training[J]. Journal of University of Science and Technology of China, 2020, 50(8): 1187-1192.

参考文献

［1］
DAI J, QI H, XIONG Y, et al. Deformable convolutional networks[C]// Proceedings of the International Conference on Computer Vision. Venice, Italy: IEEE, 2017: 764-773.
[2] HE K, GKIOXARI G, DOLLAR P, et al. Mask R-CNN[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 386-397.
[3] HUANG G, LIU Z, WEINBERGER K Q, et al. Densely connected convolutional networks[C]// Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, United states:IEEE, 2017: 2261-2269.
[4] KRIZHEVSKY A, SUTSKEVER I, HINTON G E, et al. ImageNet classification with deep convolutional neural networks[J]. Communications of The ACM. 2017,60(6):84-90.
[5] LI F F, ROB F, PIETRO P. One-shot learning of object categories[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28 (4):594-611.
[6] LAKE B M, LEE C, GLASS J, et al. One-shot learning of generative speech concepts[J]. Cognitive Science, 2014, 36(36): 803-808.
[7] GREGORY K, RICHARD Z, RUSLAN S. Siamese neural networks for one-shot image recognition[C/OL]// Proceedings of the 32nd International Conference on Machine Learning. Lille, France: JMLR:W&CP, 2015. [2020-05-18]. https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf
[8] VINYALS O, BLUNDELL C, LILLICRAP T, et al. Matching networks for one shot learning[C]// Advances in Neural Information Processing Systems 29 - Proceedings of the 2016 Conference. Barcelona, Spain:Neural information processing systems foundation, 2016,0(1): 3637-3645.
[9] SANTORO A, BARTUNOV S, BOTVINICK M, et al. Meta-learning with memory-augmented neural networks[C]// 33rd International Conference on Machine Learning. New York, United states: International Machine Learning Society,2016, 4(4): 2740-2751.
[10] CHEN Z, FU Y, WANG Y , et al. Image deformation Meta-networks for one-shot learning[C]// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Long Beach, CA, United states: IEEE, 2019: 8672-8681.
[11] LAKE B M, SALAKHUTDINOV R, TENENBAUM J B. Human-level concept learning through probabilistic program induction[J]. Science, 2015,350(6266): 1332-1338.
[12] LAKE B M, SALAKHUTDINOV R, TENENBAUM J B, et al. One-shot learning by inverting a compositional causal process[C]// Neural Information Processing Systems26,NIPS 2013. Lake Tahoe, United states: Neural information processing systems foundation ,2013: 1-9.
[13] SALAKHUTDINOV R, TENENBAUM J, TORRALBA A, et al. One-shot learning with a hierarchical nonparametric Bayesian model[C]// Workshop on Unsupervised and Transfer Learning.Washington: IEEE, 2012: 195-207.
[14] MISHRA N, ROHANINEJAD M, CHEN X, et al. A simple neural attentive meta-learner [C]// 6th International Conference on Learning Representations. Vancouver, Canada: IEEE, 2018: 1-17.
[15] RAVI S, LAROCHELLE H. Optimization as a model for few-shot learning[C]// 5th International Conference on Learning Representations. Toulon, France: IEEE, 2017: 1-11.
[16] YOON J, KIM T, DIA O, et al. Bayesian model-agnostic Meta-learning[C]// Proceedings of the Conference on Advances in Neural Information Processing Systems. Montreal, Canada: Neural information processing systems foundation, 2018, arXiv:1806.03836v4.
[17] FINN C, ABBEEL P, LEVINE S, et al. Model-agnostic Meta-learning for fast adaptation of deep networks[C]// 34th International Conference on Machine Learning. Sydney, Australia: IMLS, 2017, 3(8):1856-1868.
[18] NICHOL A, ACHIAM J, SCHULMAN J, et al. On first-order Meta-learning algorithms.[J/OL]. 2018, arXiv: abs/1803.02999.
[19] SHABAN A, BANSAL S, LIU Z, et al. One-shot learning for semantic segmentation[C]// British Machine Vision Conference 2017. London, United Kingdom: BMVA Press, 2017, arXiv:1709.03410.
[20] CAI Q, PAN Y, YAO T, et al. Memory matching networks for one-shot image recognition[C]// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Salt Lake City, United states: IEEE Computer Society, 2018:4080-4088.
[21] SNELL J, SWERSKY K, ZEMEL R S, et al. Prototypical networks for few-shot learning[C]// Advances in Neural Information Processing Systems. Long Beach, United states: IEEE, 2017: 4078-4088.
[22] SUNG F, YANG Y, ZHANG L, et al. Learning to compare: Relation network for few-shot learning[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, United states: IEEE Computer Society, 2018: 1199-1208.



训练过程中使用支持集信息的单样本学习算法

A one-shot learning algorithm using support set information during training

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价