Results 101 to 110 of about 3,042,897 (294)

Molecular Atlas of Key Food Odorants Reveals Mixture‐Level Organization and Enables Generative Aroma Design

Advanced Science, EarlyView.
KFO‐Atlas reveals how real‐world aromas are organized as structured mixtures rather than individual molecules. Building on these principles, KFO‐Gen, a generative AI framework, designs perceptually valid aroma formulations and reconstructs meat‐like aromas exclusively from plant‐derived odorants, providing a foundation for mixture‐level studies and AI ...
Jingzhi Zhang, Huadong Xing, Antonella Di Pizio, Qinfei Ke, Xingran Kou, Dachuan Zhang   +5 more
wiley   +1 more source

A survey on text generation using generative adversarial networks

, 2021
This work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks. The usage of adversarial learning for text generation is promising as it provides alternatives to generate the so ...
Papa, João P. [UNESP], de Rosa, Gustavo H. [UNESP], Papa, João P., Gustavo H. de Rosa, de Rosa, Gustavo H., João P. Papa   +5 more
core   +1 more source

Triangle Generative Adversarial Networks

CoRR, 2017
A Triangle Generative Adversarial Network ($Δ$-GAN) is developed for semi-supervised cross-domain joint distribution matching, where the training data consists of samples from each domain, and supervision of domain correspondence is provided by only a few paired samples.
Zhe Gan, Liqun Chen 0001, Weiyao Wang 0002, Yunchen Pu, Yizhe Zhang 0002, Hao Liu 0015, Chunyuan Li, Lawrence Carin   +7 more
openaire   +3 more sources

scGEN: Leveraging an Adaptive Weighting Strategy for Challenging Cell Clustering in Single‐Cell RNA‐seq

Advanced Science, EarlyView.
scGEN combines attribute–structure encoding, adaptive hard‐sample weighting, and regularized zero‐inflated negative binomial loss to resolve ambiguous single‐cell states. In the developing human pituitary, refined annotations reveal Pre.Gonado cells at 7 weeks and previously obscured developmental communication networks.
Fang Huang, Dayu Hu, Yaqi Dai, Aoyi Geng, Renxiang Guan, Hao Quan, Brett A. McGregor, Zhihan Wang, Nadeem Khan, Hongsheng Zhang, Jinghua Zhang, Mingyang Xia, Xiaoqiong Gu, Di Che, Junguk Hur, Xiaoyu Cui, Kai Guo   +16 more
wiley   +1 more source

Epistemic Generative Adversarial Networks

CoRR
Generative 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

Modular Generative Adversarial Networks [PDF]

, 2018
Existing methods for multi-domain image-to-image translation (or generation) attempt to directly map an input image (or a random vector) to an image in one of the output domains. However, most existing methods have limited scalability and robustness, since they require building independent models for each pair of domains in question.
Bo Zhao 0032, Bo Chang 0002, Zequn Jie, Leonid Sigal   +3 more
openaire   +2 more sources

Q‐LEAP: Millisecond Hyperdimensional Optimization for Full‐Spectrum Optical Metamaterials

Advanced Science, EarlyView.
Q‐LEAP integrates physics‐informed residual machine learning with factorization‐machine‐encoded quantum annealing to design full‐spectrum optical metamaterials. It explores a 2108 design space and, in a single 2.56 ms annealing step, reaches 85.83% of the theoretical FoM limit, enabling selective 5‐8 µm emission with 3–5 and 8–14 µm suppression and ∼40×
Zikang Guo, Yunwen Wu, Yuhui Guo, Shenghong Ju   +3 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, Lei You, Zeyang Zhang, Zhiwei Fan, Weiling Zhao, Nianyin Zeng, Yidong Chen, Xiaobo Zhou   +7 more
doaj   +1 more source

Dual Randomness From Spatial and Temporal Variations in Stochastic Zinc Gallate Nanoclusters for PUF and TRNG Co‐Driven Image Generation

Advanced Science, EarlyView.
A dual‐randomness hardware platform is demonstrated by integrating PUF and TRNG functionalities into mixed‐ZGO nanoclusters. By leveraging spatial and temporal stochasticity, the mixed‐ZGO NCs device enables both unique identification and random signal generation.
Seungme Kang, Wangmyung Choi, Jueun Lee, Yeong Kwon Kim, Byung Chul Jang, Wonjun Shin, Hocheon Yoo   +6 more
wiley   +1 more source

Coupled Generative Adversarial Networks

CoRR, 2016
We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast to the existing approaches, which require tuples of corresponding images in different domains in the training set, CoGAN can learn a joint distribution without any tuple of corresponding images.
Ming-Yu Liu 0001, Oncel Tuzel
openaire   +3 more sources