Results 121 to 130 of about 39,370 (305)

Rapid measurements and phase transition detections made simple by AC-GANs

SciPost Physics Core
In recent years, significant attention has been paid to using end-to-end neural networks for analyzing Monte Carlo data. However, the exploration of non-end-to-end generative adversarial neural networks remains limited.
Jiewei Ding, Ho-Kin Tang, Wing Chi Yu
doaj   +1 more source

Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces

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, Lachlan Caulfield, Eric Sauter, Alexei Nefedov, Chengwu Yang, Christof Wöll   +5 more
wiley   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

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, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Knowledge Transfer in Deep Reinforcement Learning via an RL-Specific GAN-Based Correspondence Function

IEEE Access
Deep reinforcement learning has demonstrated superhuman performance in complex decision-making tasks, but it struggles with generalization and knowledge reuse—key aspects of true intelligence. This article introduces a novel approach that modifies
Marko Ruman, Tatiana V. Guy
doaj   +1 more source

On the "steerability" of generative adversarial networks

CoRR, 2019
An open secret in contemporary machine learning is that many models work beautifully on standard benchmarks but fail to generalize outside the lab. This has been attributed to biased training data, which provide poor coverage over real world events.
Ali Jahanian 0002, Lucy Chai, Phillip Isola   +2 more
openaire   +3 more sources

Evaluation of synthetic aerial imagery using unconditional generative adversarial networks

, 2022
Image generation techniques, such as generative adversarial networks (GANs), have become sufficiently sophisticated to cause growing concerns around the authenticity of images in the public domain.
Hart, Glen, Houghton, Robert, Yates, Matthew, Pound, Michael, Torres Torres, Mercedes   +4 more
core   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

ADAPTIVE MULTIMEDIA OPTIMIZATION USING GENERATIVE ADVERSARIAL NETWORKS AND ATTENTION-BASED DEEP FEATURE LEARNING FRAMEWORK [PDF]

ICTACT Journal on Image and Video Processing
Multimedia systems have faced persistent challenges in maintaining perceptual quality under dynamic network and computational constraints. Traditional optimization techniques have struggled to preserve visual fidelity while adapting to heterogeneous ...
M. Subi Stalin, R. Prabakaran
doaj   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Generative adversarial synthetic neighbors-based unsupervised anomaly detection

Scientific Reports
Anomaly detection is crucial for the stable operation of mechanical systems, securing financial transactions, and ensuring network security, among other critical areas.
Lan Chen, Hong Jiang, Lizhong Wang, Jun Li, Manhua Yu, Yong Shen, Xusheng Du   +6 more
doaj   +1 more source