353 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
Deep Generative Modeling of LiDAR Data
Building models capable of generating structured output is a key challenge
for AI and robotics. While generative models have been explored on many types
of data, little work has been done on synthesizing lidar scans, which play a
key role in robot mapping and localization. In this work, we show that one can
adapt deep generative models for this task by unravelling lidar scans into a 2D
point map. Our approach can generate high quality samples, while simultaneously
learning a meaningful latent representation of the data. We demonstrate
significant improvements against state-of-the-art point cloud generation
methods. Furthermore, we propose a novel data representation that augments the
2D signal with absolute positional information. We show that this helps
robustness to noisy and imputed input; the learned model can recover the
underlying lidar scan from seemingly uninformative dataComment: Presented at IROS 201
A Chronological Survey of Theoretical Advancements in Generative Adversarial Networks for Computer Vision
Generative Adversarial Networks (GANs) have been workhorse generative models
for last many years, especially in the research field of computer vision.
Accordingly, there have been many significant advancements in the theory and
application of GAN models, which are notoriously hard to train, but produce
good results if trained well. There have been many a surveys on GANs,
organizing the vast GAN literature from various focus and perspectives.
However, none of the surveys brings out the important chronological aspect: how
the multiple challenges of employing GAN models were solved one-by-one over
time, across multiple landmark research works. This survey intends to bridge
that gap and present some of the landmark research works on the theory and
application of GANs, in chronological order
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