Manifold Constraint Regularization Improves Generative Adversarial Networks for Remote Sensing Image Generation

المفاهيم الأساسية

Generative Adversarial Networks (GANs) exhibit a heightened susceptibility to overfitting on remote sensing images compared to natural images. This study proposes a novel manifold constraint regularization (MCR) method that tackles the overfitting problem by encouraging the learned features to align with the structure of the real data manifold, promoting the discriminator to generalize well beyond the training data.

الملخص

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:

1. A feature distribution compactness measure based on singular values to quantify the compactness of the feature distribution.
2. A data manifold evaluation function that leverages the compactness measure to assess how well the learned features capture the underlying data manifold.

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.

الإحصائيات

The intrinsic dimension of the NWPU dataset is 52, while the intrinsic dimension of the FFHQ dataset is 35.
The FID score of StyleGAN2 on the NWPU dataset is 11.97, while the FID score on the FFHQ dataset is 3.27.

اقتباسات

"GANs are more susceptible to overfitting on RS image dataset compared to natural image dataset."
"Compared to natural images, RS images typically cover larger area, encompassing a wider variety of scenes and richer content. Consequently, we hypothesize that the intrinsic dimension of the RS dataset is larger than that of natural dataset."

الرؤى الأساسية المستخلصة من

Manifold Constraint Regularization for Remote Sensing Image Generation

by Xingzhe Su,C... في arxiv.org 03-29-2024

https://arxiv.org/pdf/2305.19507.pdf

استفسارات أعمق

How can the proposed manifold constraint regularization be extended to other generative models beyond GANs

The proposed manifold constraint regularization approach can be extended to other generative models beyond GANs by adapting the regularization term to suit the specific architecture and requirements of the model. For instance, in variational autoencoders (VAEs), the manifold constraint regularization could be incorporated into the loss function to encourage the latent space to align with the data manifold. Similarly, in flow-based models, the regularization term could be integrated to ensure that the learned transformations capture the underlying data distribution effectively. By customizing the regularization term to the characteristics and constraints of different generative models, the manifold constraint regularization approach can be applied more broadly.

What are the potential limitations of the current manifold constraint regularization approach, and how can it be further improved

One potential limitation of the current manifold constraint regularization approach is the reliance on pre-defined submanifold membership information for training. Obtaining this information can be challenging in unsupervised settings, limiting the applicability of the method. To address this limitation, future improvements could focus on developing unsupervised methods to learn the submanifold membership directly from the data. Additionally, the regularization term could be further refined to incorporate adaptive mechanisms that adjust the constraints based on the data distribution, enhancing the flexibility and effectiveness of the approach.

What other characteristics of remote sensing data, beyond the intrinsic dimension, could contribute to the overfitting problem of GANs, and how can they be addressed

Beyond the intrinsic dimension, other characteristics of remote sensing data that could contribute to the overfitting problem of GANs include spatial variability, spectral diversity, and temporal dynamics. Spatial variability refers to the wide range of landscapes and terrains captured in remote sensing images, leading to complex and diverse data distributions. Spectral diversity encompasses the different wavelengths and bands used in remote sensing, adding another layer of complexity to the data manifold. Temporal dynamics introduce changes over time, requiring models to capture variations and patterns across different time points. To address these challenges, techniques such as data augmentation, regularization, and adaptive learning mechanisms can be employed to enhance the robustness and generalization of GANs on remote sensing data.