Consistency-Regularized GAN for Few-Shot SAR Target Recognition

• URL: http://arxiv.org/abs/2601.15681v1
• Date: Thu, 22 Jan 2026 06:02:39 GMT
• Title: Consistency-Regularized GAN for Few-Shot SAR Target Recognition
• Authors: Yikui Zhai, Shikuang Liu, Wenlve Zhou, Hongsheng Zhang, Zhiheng Zhou, Xiaolin Tian, C. L. Philip Chen,
• Abstract summary: Few-shot recognition in synthetic aperture radar (SAR) imagery remains a critical bottleneck for real-world applications due to extreme data scarcity.<n>A promising strategy involves a large dataset with a generative adversarial network (GAN), pre-training a model via self-supervised learning (SSL), and then fine-tuning on the few labeled samples.<n>This approach faces a fundamental paradox: conventional GANs themselves require abundant data for stable training, contradicting the premise of few-shot learning.<n>We propose the consistency-regularized generative adversarial network (Cr-GAN), a novel framework designed to synthesize diverse, high
• Score: 40.2533418376231
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot recognition in synthetic aperture radar (SAR) imagery remains a critical bottleneck for real-world applications due to extreme data scarcity. A promising strategy involves synthesizing a large dataset with a generative adversarial network (GAN), pre-training a model via self-supervised learning (SSL), and then fine-tuning on the few labeled samples. However, this approach faces a fundamental paradox: conventional GANs themselves require abundant data for stable training, contradicting the premise of few-shot learning. To resolve this, we propose the consistency-regularized generative adversarial network (Cr-GAN), a novel framework designed to synthesize diverse, high-fidelity samples even when trained under these severe data limitations. Cr-GAN introduces a dual-branch discriminator that decouples adversarial training from representation learning. This architecture enables a channel-wise feature interpolation strategy to create novel latent features, complemented by a dual-domain cycle consistency mechanism that ensures semantic integrity. Our Cr-GAN framework is adaptable to various GAN architectures, and its synthesized data effectively boosts multiple SSL algorithms. Extensive experiments on the MSTAR and SRSDD datasets validate our approach, with Cr-GAN achieving a highly competitive accuracy of 71.21% and 51.64%, respectively, in the 8-shot setting, significantly outperforming leading baselines, while requiring only ~5 of the parameters of state-of-the-art diffusion models. Code is available at: https://github.com/yikuizhai/Cr-GAN.

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