Enhancing Recognition Accuracy of Urban Flooding by Generating Synthetic Samples Using CycleGAN
Author:
Affiliation:
1. Feng Chia University,Dept. of Information Engineering and Computer Science,Taiwan, R.O.C.
Funder
MOST
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9868970/9868972/09869172.pdf?arnumber=9869172
Reference4 articles.
1. Cycle GAN-Based Data Augmentation For Multi-Organ Detection In CT Images Via Yolo
2. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
3. The Generalized Detection Method for the Dim Small Targets by Faster R-CNN Integrated with GAN
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1. Synthetic Data-Driven Real-Time Detection Transformer Object Detection in Raining Weather Conditions;Applied Sciences;2024-06-05
2. Decomposition-based Data Augmentation for Time-series Building Load Data;Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation;2023-11-15
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