4,100 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
Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems
In this paper, we propose a new framework for mitigating biases in machine
learning systems. The problem of the existing mitigation approaches is that
they are model-oriented in the sense that they focus on tuning the training
algorithms to produce fair results, while overlooking the fact that the
training data can itself be the main reason for biased outcomes. Technically
speaking, two essential limitations can be found in such model-based
approaches: 1) the mitigation cannot be achieved without degrading the accuracy
of the machine learning models, and 2) when the data used for training are
largely biased, the training time automatically increases so as to find
suitable learning parameters that help produce fair results. To address these
shortcomings, we propose in this work a new framework that can largely mitigate
the biases and discriminations in machine learning systems while at the same
time enhancing the prediction accuracy of these systems. The proposed framework
is based on conditional Generative Adversarial Networks (cGANs), which are used
to generate new synthetic fair data with selective properties from the original
data. We also propose a framework for analyzing data biases, which is important
for understanding the amount and type of data that need to be synthetically
sampled and labeled for each population group. Experimental results show that
the proposed solution can efficiently mitigate different types of biases, while
at the same time enhancing the prediction accuracy of the underlying machine
learning model
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