8,176 research outputs found
MAGAN: Margin Adaptation for Generative Adversarial Networks
We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs)
algorithm, a novel training procedure for GANs to improve stability and
performance by using an adaptive hinge loss function. We estimate the
appropriate hinge loss margin with the expected energy of the target
distribution, and derive principled criteria for when to update the margin. We
prove that our method converges to its global optimum under certain
assumptions. Evaluated on the task of unsupervised image generation, the
proposed training procedure is simple yet robust on a diverse set of data, and
achieves qualitative and quantitative improvements compared to the
state-of-the-art
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
CONAN: Complementary Pattern Augmentation for Rare Disease Detection
Rare diseases affect hundreds of millions of people worldwide but are hard to
detect since they have extremely low prevalence rates (varying from 1/1,000 to
1/200,000 patients) and are massively underdiagnosed. How do we reliably detect
rare diseases with such low prevalence rates? How to further leverage patients
with possibly uncertain diagnosis to improve detection? In this paper, we
propose a Complementary pattern Augmentation (CONAN) framework for rare disease
detection. CONAN combines ideas from both adversarial training and max-margin
classification. It first learns self-attentive and hierarchical embedding for
patient pattern characterization. Then, we develop a complementary generative
adversarial networks (GAN) model to generate candidate positive and negative
samples from the uncertain patients by encouraging a max-margin between
classes. In addition, CONAN has a disease detector that serves as the
discriminator during the adversarial training for identifying rare diseases. We
evaluated CONAN on two disease detection tasks. For low prevalence inflammatory
bowel disease (IBD) detection, CONAN achieved .96 precision recall area under
the curve (PR-AUC) and 50.1% relative improvement over best baseline. For rare
disease idiopathic pulmonary fibrosis (IPF) detection, CONAN achieves .22
PR-AUC with 41.3% relative improvement over the best baseline
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