Results 71 to 80 of about 39,632 (294)
Sparse Generative Adversarial Network [PDF]
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019 We propose a new approach to Generative Adversarial Networks (GANs) to achieve an improved performance with additional robustness to its so-called and well recognized mode collapse. We first proceed by mapping the desired data onto a frame-based space for a sparse representation to lift any limitation of small support features prior to learning the ...
Shahin Mahdizadehaghdam +2 more
openaire +2 more sources
Advanced Materials, EarlyView.This review summarizes the principles and challenges of nonaqueous lithium‐oxygen batteries and recent advances in cathode catalysts, including carbon‐based materials, metals, oxides, sulfides, nitrides, carbides, and redox mediators. It highlights emerging design strategies and artificial intelligence‐driven approaches, emphasizing data‐assisted ...
Yuqing Yao +8 more
wiley +1 more source
Advanced Materials Technologies, EarlyView.The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz +5 more
wiley +1 more source
Attentively Conditioned Generative Adversarial Network for Semantic Segmentation
IEEE Access, 2020 Generative Adversarial Network has proven to produce state-of-the-art results by framing a generative modeling task into a supervised learning problem. In this paper, we propose Attentively Conditioned Generative Adversarial Network (ACGAN) for semantic ...
Ariyo Oluwasanmi +5 more
doaj +1 more source
Predicting novel views using generative adversarial query network [PDF]
, 2019 The problem of predicting a novel view of the scene using an arbitrary number of observations is a challenging problem for computers as well as for humans.
Nguyen-Ha, P. (Phong) +11 more
core +1 more source
Generative Adversarial Networks: An Overview [PDF]
IEEE Signal Processing Magazine, 2018 Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image ...
Antonia Creswell +5 more
openaire +3 more sources
Hyperbolic Generative Adversarial Network
CoRR, 2021 Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning have gained popularity because of their ability to represent hierarchical data. We propose that it is possible to take advantage of the hierarchical characteristic present in the images by using hyperbolic neural networks in a GAN architecture.
Diego Lazcano +2 more
openaire +2 more sources
Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space
Advanced Robotics Research, EarlyView.A quadruped robot masters dynamic jumps through constrained spaces with animal‐inspired moves and intelligent vision control. This hierarchical learning approach combines imitation of biological agility with real‐time trajectory planning. Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating ...
Zeren Luo +6 more
wiley +1 more source
Optoelectronic generative adversarial networks
Communications PhysicsRecent breakthroughs in artificial intelligence generative content technology are driving transformational change. Diffractive optical networks offer a promising solution for high-speed, low-power generative models.
Jumin Qiu +5 more
doaj +1 more source
Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling [PDF]
, 2017 We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convo-lutional networks and ...
Wu, Jiajun +4 more
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