3,260 research outputs found
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
DISCO Nets: DISsimilarity COefficient Networks
We present a new type of probabilistic model which we call DISsimilarity
COefficient Networks (DISCO Nets). DISCO Nets allow us to efficiently sample
from a posterior distribution parametrised by a neural network. During
training, DISCO Nets are learned by minimising the dissimilarity coefficient
between the true distribution and the estimated distribution. This allows us to
tailor the training to the loss related to the task at hand. We empirically
show that (i) by modeling uncertainty on the output value, DISCO Nets
outperform equivalent non-probabilistic predictive networks and (ii) DISCO Nets
accurately model the uncertainty of the output, outperforming existing
probabilistic models based on deep neural networks
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