CLC number: TP39
On-line Access: 2024-07-30
Received: 2023-07-21
Revision Accepted: 2024-07-30
Crosschecked: 2023-12-17
Cited: 0
Clicked: 596
Citations: Bibtex RefMan EndNote GB/T7714
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.
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