Results 101 to 110 of about 2,921,969 (298)
Epistemic Generative Adversarial Networks
CoRRGenerative models, particularly Generative Adversarial Networks (GANs), often suffer from a lack of output diversity, frequently generating similar samples rather than a wide range of variations. This paper introduces a novel generalization of the GAN loss function based on Dempster-Shafer theory of evidence, applied to both the generator and ...
Muhammad Mubashar, Fabio Cuzzolin
openaire +2 more sources
Annealed Generative Adversarial Networks
CoRR, 2017 9 pages, 6 ...
Arash Mehrjou +2 more
openaire +2 more sources
Advanced Science, EarlyView.A latent diffusion‐based framework is proposed for designing functionally graded metamaterials with perfect connectivity. By integrating vector‐quantized latent representations with mechanistic guidance, the framework enables accurate inverse design toward target elastic properties.
Jongbin Yu, Dosung Lee, Namjung Kim
wiley +1 more source
Advanced Science, EarlyView.Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi +7 more
wiley +1 more source
Generative Adversarial Networks and Its Applications in Biomedical Informatics
Frontiers in Public Health, 2020 The basic Generative Adversarial Networks (GAN) model is composed of the input vector, generator, and discriminator. Among them, the generator and discriminator are implicit function expressions, usually implemented by deep neural networks. GAN can learn
Lan Lan +7 more
doaj +1 more source
Adversarial scheduling analysis of Game-Theoretic Models of Norm Diffusion. [PDF]
In (Istrate et al. SODA 2001) we advocated the investigation of robustness of results in the theory of learning in games under adversarial scheduling models.
Istrate, Gabriel +2 more
core
Training Generative Adversarial Networks via Stochastic Nash Games
, 2023 Generative adversarial networks (GANs) are a class of generative models with two antagonistic neural networks: a generator and a discriminator. These two neural networks compete against each other through an adversarial process that can be modeled as a ...
Franci, Barbara +5 more
core +2 more sources
Unrolled Generative Adversarial Networks
CoRR, 2016 We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator's objective, which is ideal but infeasible in practice, and using the current value of ...
Luke Metz +3 more
openaire +3 more sources
Neuromorphic Devices and Computing for Sensing, Memory, and Control
Advanced Science, EarlyView.This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu +2 more
wiley +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
core