Results 111 to 120 of about 219,974 (297)
Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN
, 2017 We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks.
Han, Sungyeob +2 more
core
Advanced Science, EarlyView.ABSTRACT Conventional software‐based encryption faces mounting limitations in power efficiency and security, inspiring the development of emerging neuromorphic computing hardware encryption. This study presents a hardware‐level multi‐dimensional encryption paradigm utilizing optoelectronic neuromorphic devices with low energy consumption of 3.3 fJ ...
Bo Sun +3 more
wiley +1 more source
Generative Adversarial Networks in Speech Enhancement: A Survey
IEEE AccessGenerative adversarial networks are a powerful type of model in deep learning. They have been successfully applied within different domains. This review focuses on the usage of generative adversarial networks for speech enhancement.
Justina Ramonaite +2 more
doaj +1 more source
A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series
, 2019 This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases.
Cheng, Cheng +4 more
core
Advanced Science, EarlyView.SpaMode introduces a versatile framework for spatial multi‐omics integration across vertical, horizontal, and mosaic scenarios. By disentangling modality‐invariant and variant features through a mixture‐of‐experts mechanism, it adaptively reconfigures spatially heterogeneous signals.
Xubin Zheng +6 more
wiley +1 more source
Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution
Advanced Intelligent Discovery, EarlyView.Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren +6 more
wiley +1 more source
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
, 2016 This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that also maximizes
Abbeel, Pieter +5 more
core
Advanced Intelligent Discovery, EarlyView.Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz +2 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali +5 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian +12 more
wiley +1 more source