56 research outputs found
Quality Aware Generative Adversarial Networks
Generative Adversarial Networks (GANs) have become a very popular tool for
implicitly learning high-dimensional probability distributions. Several
improvements have been made to the original GAN formulation to address some of
its shortcomings like mode collapse, convergence issues, entanglement, poor
visual quality etc. While a significant effort has been directed towards
improving the visual quality of images generated by GANs, it is rather
surprising that objective image quality metrics have neither been employed as
cost functions nor as regularizers in GAN objective functions. In this work, we
show how a distance metric that is a variant of the Structural SIMilarity
(SSIM) index (a popular full-reference image quality assessment algorithm), and
a novel quality aware discriminator gradient penalty function that is inspired
by the Natural Image Quality Evaluator (NIQE, a popular no-reference image
quality assessment algorithm) can each be used as excellent regularizers for
GAN objective functions. Specifically, we demonstrate state-of-the-art
performance using the Wasserstein GAN gradient penalty (WGAN-GP) framework over
CIFAR-10, STL10 and CelebA datasets.Comment: 10 pages, NeurIPS 201
Sliced Wasserstein Generative Models
In generative modeling, the Wasserstein distance (WD) has emerged as a useful
metric to measure the discrepancy between generated and real data
distributions. Unfortunately, it is challenging to approximate the WD of
high-dimensional distributions. In contrast, the sliced Wasserstein distance
(SWD) factorizes high-dimensional distributions into their multiple
one-dimensional marginal distributions and is thus easier to approximate. In
this paper, we introduce novel approximations of the primal and dual SWD.
Instead of using a large number of random projections, as it is done by
conventional SWD approximation methods, we propose to approximate SWDs with a
small number of parameterized orthogonal projections in an end-to-end deep
learning fashion. As concrete applications of our SWD approximations, we design
two types of differentiable SWD blocks to equip modern generative
frameworks---Auto-Encoders (AE) and Generative Adversarial Networks (GAN). In
the experiments, we not only show the superiority of the proposed generative
models on standard image synthesis benchmarks, but also demonstrate the
state-of-the-art performance on challenging high resolution image and video
generation in an unsupervised manner.Comment: This paper is accepted by CVPR 2019, accidentally uploaded as a new
submission (arXiv:1904.05408, which has been withdrawn). The code is
available at this https URL https:// github.com/musikisomorphie/swd.gi
A Systematic Survey of Regularization and Normalization in GANs
Generative Adversarial Networks (GANs) have been widely applied in different
scenarios thanks to the development of deep neural networks. The original GAN
was proposed based on the non-parametric assumption of the infinite capacity of
networks. However, it is still unknown whether GANs can generate realistic
samples without any prior information. Due to the overconfident assumption,
many issues remain unaddressed in GANs' training, such as non-convergence, mode
collapses, gradient vanishing. Regularization and normalization are common
methods of introducing prior information to stabilize training and improve
discrimination. Although a handful number of regularization and normalization
methods have been proposed for GANs, to the best of our knowledge, there exists
no comprehensive survey which primarily focuses on objectives and development
of these methods, apart from some in-comprehensive and limited scope studies.
In this work, we conduct a comprehensive survey on the regularization and
normalization techniques from different perspectives of GANs training. First,
we systematically describe different perspectives of GANs training and thus
obtain the different objectives of regularization and normalization. Based on
these objectives, we propose a new taxonomy. Furthermore, we compare the
performance of the mainstream methods on different datasets and investigate the
regularization and normalization techniques that have been frequently employed
in SOTA GANs. Finally, we highlight potential future directions of research in
this domain
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