This paper identifies a previously overlooked issue that GANs are more prone to overfitting on remote sensing (RS) image datasets compared to natural image datasets. The authors analyze the characteristics of RS images and find that they have a higher intrinsic dimension than natural images, leading to the discriminator easily overfitting to the RS datasets.
To address this challenge, the authors propose a novel manifold constraint regularization (MCR) method. MCR consists of two key components:
The authors integrate the MCR regularization term into the GAN loss functions. This approach encourages the discriminator to focus on capturing the real data manifold rather than simply memorizing the training data, thereby mitigating overfitting. Additionally, minimizing the MCR term guides the generator to align its output manifold with that of the real images, enhancing the quality of the generated images.
Extensive experiments across multiple RS datasets and GAN models demonstrate the effectiveness of the proposed method. Compared to standard GAN models and related techniques for solving overfitting, MCR achieves a 3.13% improvement in Frechet Inception Distance (FID) score and a 3.76% increase in classification accuracy on downstream tasks.
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by Xingzhe Su,C... om arxiv.org 03-29-2024
https://arxiv.org/pdf/2305.19507.pdfDiepere vragen