5,705 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
Scenario Generation for Cooling, Heating, and Power Loads Using Generative Moment Matching Networks
Scenario generations of cooling, heating, and power loads are of great
significance for the economic operation and stability analysis of integrated
energy systems. In this paper, a novel deep generative network is proposed to
model cooling, heating, and power load curves based on a generative moment
matching networks (GMMN) where an auto-encoder transforms high-dimensional load
curves into low-dimensional latent variables and the maximum mean discrepancy
represents the similarity metrics between the generated samples and the real
samples. After training the model, the new scenarios are generated by feeding
Gaussian noises to the scenario generator of the GMMN. Unlike the explicit
density models, the proposed GMMN does not need to artificially assume the
probability distribution of the load curves, which leads to stronger
universality. The simulation results show that the GMMN not only fits the
probability distribution of multi-class load curves well, but also accurately
captures the shape (e.g., large peaks, fast ramps, and fluctuation),
frequency-domain characteristics, and temporal-spatial correlations of cooling,
heating, and power loads. Furthermore, the energy consumption of generated
samples closely resembles that of real samples.Comment: This paper has been accepted by CSEE Journal of Power and Energy
System
Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture
This paper addresses the problem of interpolating visual textures. We
formulate this problem by requiring (1) by-example controllability and (2)
realistic and smooth interpolation among an arbitrary number of texture
samples. To solve it we propose a neural network trained simultaneously on a
reconstruction task and a generation task, which can project texture examples
onto a latent space where they can be linearly interpolated and projected back
onto the image domain, thus ensuring both intuitive control and realistic
results. We show our method outperforms a number of baselines according to a
comprehensive suite of metrics as well as a user study. We further show several
applications based on our technique, which include texture brush, texture
dissolve, and animal hybridization.Comment: Accepted to CVPR'1
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