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Does YOLO Really Need to See Every Training Image in Every Epoch?
Xingxing Xie, Jiahua Dong, Junwei Han +1
YOLO detectors are known for their fast inference speed, yet training them remains unexpectedly time-consuming due to their exhaustive pipeline that processes every training image…
Knowledge-Informed Neural Network for Complex-Valued SAR Image Recognition
Haodong Yang, Zhongling Huang, Shaojie Guo +3
Deep learning models for complex-valued Synthetic Aperture Radar (CV-SAR) image recognition are fundamentally constrained by a representation trilemma under data-limited and domain…
-GAN: Physics-Inspired GAN for Generating SAR Images Under Limited Data
Xidan Zhang, Yihan Zhuang, Qian Guo +5
Approaches for improving generative adversarial networks (GANs) training under a few samples have been explored for natural images. However, these methods have limited effectivenes…
Physics-Guided Detector for SAR Airplanes
Zhongling Huang, Long Liu, Shuxin Yang +3
The disperse structure distributions (discreteness) and variant scattering characteristics (variability) of SAR airplane targets lead to special challenges of object detection and…
X-Fake: Juggling Utility Evaluation and Explanation of Simulated SAR Images
Zhongling Huang, Yihan Zhuang, Zipei Zhong +3
SAR image simulation has attracted much attention due to its great potential to supplement the scarce training data for deep learning algorithms. Consequently, evaluating the quali…