DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion
- URL: http://arxiv.org/abs/2305.14849v2
- Date: Tue, 6 Jun 2023 06:35:25 GMT
- Title: DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion
- Authors: Taesun Yeom, Minhyeok Lee
- Abstract summary: Class-conditional image generation using generative adversarial networks (GANs) has been investigated through various techniques.
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 outperforms state-of-the-art conditional GAN models for image generation in terms of performance.
- Score: 2.458437232470188
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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