1,657 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
Learning a Hierarchical Latent-Variable Model of 3D Shapes
We propose the Variational Shape Learner (VSL), a generative model that
learns the underlying structure of voxelized 3D shapes in an unsupervised
fashion. Through the use of skip-connections, our model can successfully learn
and infer a latent, hierarchical representation of objects. Furthermore,
realistic 3D objects can be easily generated by sampling the VSL's latent
probabilistic manifold. We show that our generative model can be trained
end-to-end from 2D images to perform single image 3D model retrieval.
Experiments show, both quantitatively and qualitatively, the improved
generalization of our proposed model over a range of tasks, performing better
or comparable to various state-of-the-art alternatives.Comment: Accepted as oral presentation at International Conference on 3D
Vision (3DV), 201
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