DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion Article Swipe

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Taesun Yeom , Minhyeok Lee · YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2305.14849

Class-conditional image generation using generative adversarial networks (GANs) has been investigated through various techniques; however, it continues to face challenges such as mode collapse, training instability, and low-quality output in cases of datasets with high intra-class variation. Furthermore, most GANs often converge in larger iterations, resulting in poor iteration efficacy in training procedures. While Diffusion-GAN has shown potential in generating realistic samples, it has a critical limitation in generating class-conditional samples. To overcome these limitations, we propose a novel approach for class-conditional image generation using GANs called DuDGAN, which incorporates a dual diffusion-based noise injection process. Our method consists of three unique networks: a discriminator, a generator, and a classifier. During the training process, Gaussian-mixture noises are injected into the two noise-aware networks, the discriminator and the classifier, in distinct ways. This noisy data helps to prevent overfitting by gradually introducing more challenging tasks, leading to improved model performance. As a result, our method outperforms state-of-the-art conditional GAN models for image generation in terms of performance. We evaluated our method using the AFHQ, Food-101, and CIFAR-10 datasets and observed superior results across metrics such as FID, KID, Precision, and Recall score compared with comparison models, highlighting the effectiveness of our approach.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2305.14849

PDF

https://arxiv.org/pdf/2305.14849

OA Status

green

Cited By

2

Related Works

10

OpenAlex ID

https://openalex.org/W4378474092

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OpenAlex ID

https://openalex.org/W4378474092

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DOI

https://doi.org/10.48550/arxiv.2305.14849

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Title

DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion

Work title

Type

preprint

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Language

en

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Publication year

2023

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Publication date

2023-05-24

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Authors

Taesun Yeom, Minhyeok Lee

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Landing page

https://arxiv.org/abs/2305.14849

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PDF URL

https://arxiv.org/pdf/2305.14849

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Open access

Yes

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OA status

green

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OA URL

https://arxiv.org/pdf/2305.14849

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Concepts

Discriminator, Computer science, Overfitting, Classifier (UML), Artificial intelligence, Pattern recognition (psychology), Machine learning, Algorithm, Artificial neural network, Telecommunications, Detector

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Cited by

2

Total citation count in OpenAlex

Citations by year (recent)

2024: 2

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Related works (count)

10

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