22,108 research outputs found
DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion
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
Radio Galaxy Classification with wGAN-Supported Augmentation
Novel techniques are indispensable to process the flood of data from the new
generation of radio telescopes. In particular, the classification of
astronomical sources in images is challenging. Morphological classification of
radio galaxies could be automated with deep learning models that require large
sets of labelled training data. Here, we demonstrate the use of generative
models, specifically Wasserstein GANs (wGAN), to generate artificial data for
different classes of radio galaxies. Subsequently, we augment the training data
with images from our wGAN. We find that a simple fully-connected neural network
for classification can be improved significantly by including generated images
into the training set.Comment: 10 pages, 6 figures; accepted to ml.astro; v2: matches published
versio
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Generative Adversarial Networks (GANs) is a novel class of deep generative
models which has recently gained significant attention. GANs learns complex and
high-dimensional distributions implicitly over images, audio, and data.
However, there exists major challenges in training of GANs, i.e., mode
collapse, non-convergence and instability, due to inappropriate design of
network architecture, use of objective function and selection of optimization
algorithm. Recently, to address these challenges, several solutions for better
design and optimization of GANs have been investigated based on techniques of
re-engineered network architectures, new objective functions and alternative
optimization algorithms. To the best of our knowledge, there is no existing
survey that has particularly focused on broad and systematic developments of
these solutions. In this study, we perform a comprehensive survey of the
advancements in GANs design and optimization solutions proposed to handle GANs
challenges. We first identify key research issues within each design and
optimization technique and then propose a new taxonomy to structure solutions
by key research issues. In accordance with the taxonomy, we provide a detailed
discussion on different GANs variants proposed within each solution and their
relationships. Finally, based on the insights gained, we present the promising
research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table
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