552 research outputs found
Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders
Generative models and inferential autoencoders mostly make use of
norm in their optimization objectives. In order to generate perceptually better
images, this short paper theoretically discusses how to use Structural
Similarity Index (SSIM) in generative models and inferential autoencoders. We
first review SSIM, SSIM distance metrics, and SSIM kernel. We show that the
SSIM kernel is a universal kernel and thus can be used in unconditional and
conditional generated moment matching networks. Then, we explain how to use
SSIM distance in variational and adversarial autoencoders and unconditional and
conditional Generative Adversarial Networks (GANs). Finally, we propose to use
SSIM distance rather than norm in least squares GAN.Comment: Accepted (to appear) in International Conference on Image Analysis
and Recognition (ICIAR) 2020, Springe
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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