LDA-GAN: Lightweight domain-attention GAN for unpaired image-to-image translation

Zhao, Jin; Lee, Feifei; Hu, Chunyan; Yu, Hongliu; Qiu, Chen

doi:10.1016/j.neucom.2022.07.084

Cited by 9 publications

(2 citation statements)

References 8 publications

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“…In this framework nothing is application specific, thereby making this setup considerably simpler. For unpaired image translation, Zhao et al [32] presented a lightweight domain-attention generative adversarial network (LDA-GAN). This GAN uses an enhanced domainattention module (DAM) to create a longer range dependency between two domains while using fewer parameters and less memory.…”

Section: Related Workmentioning

confidence: 99%

Mathematical analysis of loss function of GAN and its loss function variants

2022

IJATEE

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Generative adversarial networks (GANs) have turned up as the most widely used approaches for creating realistic samples. They're the effective latent variable models for learning complex real distributions. However, despite their enormous success and popularity, the process of training GANs remains challenging and suffers from a number of failures. These failures include mode collapse where the generator generates the same set of output for different inputs which finally leads to loss of diversity; non-convergence because of the diverging and oscillatory behaviors of both generator and discriminator while being trained; and vanishing or exploding gradients due to which either no learning or extremely slow learning takes place. In the past years, a variety of strategies for stabilizing GAN training have been explored which includes modified architectures, loss functions, and other methods. The choice of loss function has been found to be the most crucial part of the GAN model because it influences the vanishing gradient and model collapse directly. Viewing these loss functions as divergence minimization has provided a rich avenue of development. All of these factors make GAN training inherently unstable, and this instability is difficult to comprehend mathematically. This paper intends to provide a thorough mathematical explanation of these divergence minimization functions. It illustrates in great detail the two variants of the loss functions of the original GAN, their optimization to kullback-leibler (KL) divergence and jensen-shannon (JS) divergence along with their shortcomings. It also describes the loss functions of the different loss function GAN variants that have been proposed to mitigate these shortcomings as well as their minimization. The original GAN and its loss function variants have also been implemented using the standard MNIST, Fashion-MNIST, and CIFAR-10 datasets.

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Section: Related Workmentioning

confidence: 99%

Mathematical analysis of loss function of GAN and its loss function variants

2022

IJATEE

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“…Meanwhile, GDWCT [5] excelled in arbitrary style transfer by regularizing group-based style features, reducing computational expense. Also, LDA-GAN successfully transformed key object features for high-quality images [6]. Luan et al introduced comprehensive style transfer to match texture and color, maintaining spatial consistency using a two-pass algorithm for seamless object synthesis [7].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Harmonization Method for Fine-Grained Ship Classification

Zhang,

Zhong,

Wei

et al. 2024

Remote Sensing

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Target recognition and fine-grained ship classification in remote sensing face challenges of high inter-class similarity and sample scarcity. A transfer fusion-based ship image harmonization algorithm is proposed to overcome these challenges. This algorithm designs a feature transfer fusion strategy based on the combination of a region-aware instantiation and attention mechanism. Adversarial learning is implemented through an image harmony generator and discriminator module to generate realistic remote sensing ship harmony images. Furthermore, the domain encoder and domain discriminator modules are responsible for extracting feature representations of the foreground and background, and further align the ship foreground with remote sensing ocean background features through feature discrimination. Compared with other advanced image conversion techniques, our algorithm delivers more realistic visuals, improving classification accuracy for six ship types by 3% and twelve types by 2.94%, outperforming Sim2RealNet. Finally, a mixed dataset containing data augmentation and harmonizing samples and real data was proposed for the fine-grained classification task of remote sensing ships. Evaluation experiments were conducted on eight typical fine-grained classification algorithms, and the accuracy of the fine-grained classification for all categories of ships was analyzed. The experimental results show that the mixed dataset proposed in this paper effectively alleviates the long-tail problem in real datasets, and the proposed remote sensing ship data augmentation framework performs better than state-of-the-art data augmentation methods in fine-grained ship classification tasks.

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Multi-scale information fusion generative adversarial network for real-world noisy image denoising

Hu,

Zhao

2024

Machine Vision and Applications

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LDA-GAN: Lightweight domain-attention GAN for unpaired image-to-image translation

Cited by 9 publications

References 8 publications

Mathematical analysis of loss function of GAN and its loss function variants

Mathematical analysis of loss function of GAN and its loss function variants

Remote Sensing Image Harmonization Method for Fine-Grained Ship Classification

Multi-scale information fusion generative adversarial network for real-world noisy image denoising

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