Full Text:   <568>

Summary:  <248>

Suppl. Mater.: 

CLC number: TP39

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2023-12-17

Cited: 0

Clicked: 828

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Junzhi YU

https://orcid.org/0000-0002-6347-572X

Xiali LI

https://orcid.org/0000-0001-7950-6204

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.7 P.924-937

http://doi.org/10.1631/FITEE.2300493


TibetanGoTinyNet: a lightweight U-Net style network for zero learning of Tibetan Go


Author(s):  Xiali LI, Yanyin ZHANG, Licheng WU, Yandong CHEN, Junzhi YU

Affiliation(s):  Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance, Ministry of Education, Minzu University of China, Beijing 100081, China; more

Corresponding email(s):   xiaer_li@163.com, 18560711191@163.com, wulicheng@tsinghua.edu.cn, chenyd2022@163.com, junzhi.yu@ia.ac.cn

Key Words:  Zero learning, Tibetan Go, U-Net, Self-attention mechanism, Capsule network, Monte-Carlo tree search


Xiali LI, Yanyin ZHANG, Licheng WU, Yandong CHEN, Junzhi YU. TibetanGoTinyNet: a lightweight U-Net style network for zero learning of Tibetan Go[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(7): 924-937.

@article{title="TibetanGoTinyNet: a lightweight U-Net style network for zero learning of Tibetan Go",
author="Xiali LI, Yanyin ZHANG, Licheng WU, Yandong CHEN, Junzhi YU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="25",
number="7",
pages="924-937",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300493"
}

%0 Journal Article
%T TibetanGoTinyNet: a lightweight U-Net style network for zero learning of Tibetan Go
%A Xiali LI
%A Yanyin ZHANG
%A Licheng WU
%A Yandong CHEN
%A Junzhi YU
%J Frontiers of Information Technology & Electronic Engineering
%V 25
%N 7
%P 924-937
%@ 2095-9184
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300493

TY - JOUR
T1 - TibetanGoTinyNet: a lightweight U-Net style network for zero learning of Tibetan Go
A1 - Xiali LI
A1 - Yanyin ZHANG
A1 - Licheng WU
A1 - Yandong CHEN
A1 - Junzhi YU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 7
SP - 924
EP - 937
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300493


Abstract: 
The game of tibetan Go faces the scarcity of expert knowledge and research literature. Therefore, we study the zero learning model of tibetan Go under limited computing power resources and propose a novel scale-invariant u-Net style two-headed output lightweight network TibetanGoTinyNet. The lightweight convolutional neural networks and capsule structure are applied to the encoder and decoder of TibetanGoTinyNet to reduce computational burden and achieve better feature extraction results. Several autonomous self-attention mechanisms are integrated into TibetanGoTinyNet to capture the tibetan Go board’s spatial and global information and select important channels. The training data are generated entirely from self-play games. TibetanGoTinyNet achieves 62%-78% winning rate against other four u-Net style models including Res-UNet, Res-UNet Attention, Ghost-UNet, and Ghost Capsule-UNet. It also achieves 75% winning rate in the ablation experiments on the attention mechanism with embedded positional information. The model saves about 33% of the training time with 45%-50% winning rate for different monte-Carlo tree search (MCTS) simulation counts when migrated from 9×9 to 11×11 boards. Code for our model is available at https://github.com/paulzyy/TibetanGoTinyNet.

TibetanGoTinyNet:一种应用于藏式围棋的U型网络风格的轻量级零学习模型

李霞丽1,2,张焱垠1,2,吴立成1,2,陈彦东1,2,喻俊志3
1中央民族大学民族语言智能分析与安全治理教育部重点实验室,中国北京市,100081
2中央民族大学信息工程学院,中国北京市,100081
3北京大学工学院先进制造与机器人系,中国北京市,100871
摘要:藏式围棋面临专家知识和研究文献匮乏的问题。因此,我们研究了有限计算能力资源下藏式围棋的零学习模型,并提出一种新颖的尺度不变U型网络(U-Net)风格的双头输出轻量级网络TibetanGoTinyNet。该网络的编码和解码器应用了轻量级卷积神经网络(CNN)和胶囊网络,以减少计算负担并提升特征提取效果。网络中集成了数种自注意力机制,以捕获藏式围棋棋盘的空间和全局信息,并选择有价值通道。训练数据完全由自我对弈生成。TibetanGoTinyNet在与Res-UNet,Res-UNet Attention,Ghost-UNet和Ghost Capsule-UNet 4个U-Net风格模型的对弈中获得了62%–78%的胜率。在捕获棋盘位置信息的轻量级自注意机制消融实验中,它也实现了75%的胜率。当模型从9×9棋盘直接迁移到11×11棋盘时,该模型在不同的蒙特卡洛树搜索(MCTS)次数下节省了约33%的训练时间,并获得了45%–50%的胜率。本文模型代码可在https://github.com/paulzyy/TibetanGoTinyNet上获取。

关键词:零学习;藏式围棋;U型网络;自注意力机制;胶囊网络;蒙特卡洛树搜索

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Azad R, Bozorgpour IA, Asadi-Aghbolaghi M, et al., 2021. Deep frequency re-calibration U-Net for medical image segmentation. IEEE/CVF Int Conf on Computer Vision Workshops, p.3267-3276.

[2]Azad R, Aghdam EK, Rauland A, et al., 2022a. Medical image segmentation review: the success of U-Net.

[3]Azad R, Khosravi N, Merhof D, 2022b. SMU-Net: style matching U-Net for brain tumor segmentation with missing modalities. https://arxiv.org/abs/2204.02961v1

[4]Bougourzi F, Distante C, Dornaika F, et al., 2023. PDAtt-Unet: pyramid dual-decoder attention Unet for Covid-19 infection segmentation from CT-scans. Med Image Anal, 86:102797.

[5]Ding XW, Wang SS, 2021. Efficient Unet with depth-aware gated fusion for automatic skin lesion segmentation. J Intell Fuzzy Syst, 40(5):9963-9975.

[6]Gao YF, Wu LZ, Li HY, 2021. GomokuNet: a novel UNet-style network for Gomoku zero learning via exploiting positional information and multiscale features. IEEE Conf on Games, p.1-4.

[7]Guo CL, Szemenyei M, Yi YG, et al., 2021. SA-UNet: spatial attention U-Net for retinal vessel segmentation. 25th Int Conf on Pattern Recognition, p.1236-1242.

[8]Guo YH, Cai B, Liang PP, et al., 2022. Efficient network with ghost tied block for heart segmentation. Proc SPIE 12032, Medical Imaging 2022: Image Processing, Article 120320A.

[9]Hai JJ, Qiao K, Chen J, et al., 2019. Fully convolutional DenseNet with multiscale context for automated breast tumor segmentation. J Healthc Eng, 2019:8415485.

[10]Han K, Wang YH, Tian Q, et al., 2020. GhostNet: more features from cheap operations. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.1577-1586.

[11]He KM, Zhang XY, Ren SQ, et al., 2016. Deep residual learning for image recognition. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.770-778.

[12]Heidler K, Mou LC, Baumhoer C, et al., 2022. HED-UNet: combined segmentation and edge detection for monitoring the Antarctic coastline. IEEE Trans Geosci Remote Sens, 60:4300514.

[13]Hou QB, Zhou DQ, Feng JS, 2021. Coordinate attention for efficient mobile network design. IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.13708-13717.

[14]Howard AG, Zhu ML, Chen B, et al., 2017. MobileNets: efficient convolutional neural networks for mobile vision applications.

[15]Hu J, Shen L, Sun G, 2018. Squeeze-and-excitation networks. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.7132-7141.

[16]Huang G, Liu Z, Van Der Maaten L, et al., 2017. Densely connected convolutional networks. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2261-2269.

[17]Huang Z, Zhao YW, Liu YH, et al., 2021. GCAUNet: a group cross-channel attention residual UNet for slice based brain tumor segmentation. Biomed Signal Process Contr, 70:102958.

[18]Ibtehaz N, Rahman MS, 2020. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neur Netw, 121:74-87.

[19]Jing JF, Wang Z, Rätsch M, et al., 2022. Mobile-Unet: an efficient convolutional neural network for fabric defect detection. Text Res J, 92(1-2):30-42.

[20]Kazerouni IA, Dooly G, Toal D, 2021. Ghost-UNet: an asymmetric encoder-decoder architecture for semantic segmentation from scratch. IEEE Access, 9:97457-97465.

[21]Kocsis L, Szepesvári C, 2006. Bandit based Monte-Carlo planning. 17th European Conf on Machine Learning, p.282-293.

[22]Mamoon S, Manzoor MA, Zhang FE, et al., 2020. SPSSNet: a real-time network for image semantic segmentation. Front Inform Technol Electron Eng, 21(12):1770-1782.

[23]Ronneberger O, Fischer P, Brox T, 2015. U-Net: convolutional networks for biomedical image segmentation. 18th Int Conf on Medical Image Computing and Computer-Assisted Intervention, p.234-241.

[24]Sabour S, Frosst N, Hinton GE, 2017. Dynamic routing between capsules. Proc 31st Int Conf on Neural Information Processing Systems, p.3859-3869.

[25]Saeed MU, Ali G, Bin W, et al., 2021. RMU-Net: a novel residual mobile U-Net model for brain tumor segmentation from MR images. Electronics, 10(16):1962.

[26]Silver D, Huang A, Maddison CJ, et al., 2016. Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587):484-489.

[27]Silver D, Hubert T, Schrittwieser J, et al., 2017a. Mastering chess and shogi by self-play with a general reinforcement learning algorithm.

[28]Silver D, Schrittwieser J, Simonyan K, et al., 2017b. Mastering the game of Go without human knowledge. Nature, 550(7676):354-359.

[29]Soemers DJNJ, Piette É, Stephenson M, et al., 2022. The Ludii game description language is universal.

[30]Tan MX, Le Q, 2019. EfficientNet: rethinking model scaling for convolutional neural networks. Proc 36th Int Conf on Machine Learning, p.6105-6114.

[31]Tang YH, Han K, Guo JY, et al., 2022. GhostNetV2: enhance cheap operation with long-range attention. Proc 36th Int Conf on Neural Information Processing Systems.

[32]Tian MJ, Li XL, Kong SH, et al., 2022. A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot. Front Inform Technol Electron Eng, 23(8):1217-1228.

[33]Tran M, Vo-Ho VK, Le NTH, 2022. 3DConvCaps: 3DUnet with convolutional capsule encoder for medical image segmentation. 26th Int Conf on Pattern Recognition, p.4392-4398.

[34]Trebing K, Staùczyk T, Mehrkanoon S, 2021. SmaAt-UNet: precipitation nowcasting using a small attention-UNet architecture. Patt Recogn Lett, 145:178-186.

[35]Woo S, Park J, Lee JY, et al., 2018. CBAM: convolutional block attention module. Proc 15th European Conf on Computer Vision, p.3-19.

[36]Wu YH, Gao SH, Mei J, et al., 2021. JCS: an explainable COVID-19 diagnosis system by joint classification and segmentation. IEEE Trans Image Process, 30:3113-3126.

[37]Xu YH, Li Q, He SY, et al., 2022. Ghost-Unet: an efficient convolutional neural network for spine MR image segmentation: lightweight segmentation method for spine MRI. Proc 4th Int Conf on Robotics, Intelligent Control and Artificial Intelligence, p.1159-1163.

[38]Xue LY, Lin JW, Cao XR, et al., 2019. A saliency and Gaussian net model for retinal vessel segmentation. Front Inform Technol Electron Eng, 20(8):1075-1086.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE