Results 91 to 100 of about 16,046 (295)
MR-GAN: Manifold Regularized Generative Adversarial Networks
CoRR, 2018 arXiv admin note: text overlap with arXiv:1706.04156 by other ...
Qunwei Li +5 more
openaire +2 more sources
Big Data and Cognitive Computing, 2020 In this work, we propose a novel defense system against adversarial examples leveraging the unique power of Generative Adversarial Networks (GANs) to generate new adversarial examples for model retraining. To do so, we develop an automated pipeline using
Shayan Taheri +3 more
doaj +1 more source
Synthesising training data with generative adversarial networks (GANs) in computed tomography perfusion. [PDF]
, 2021 Brain stroke is seen as a very vital problem due to its possible health consequences and incidence. It is the second cause of premature death worldwide.
Korkmaz Murat
core
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 +4 more
wiley +1 more source
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
CoRR, 2018 Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability. However, they have not been well visualized or understood. How does a GAN represent our visual world internally?
David Bau +6 more
openaire +3 more sources
Improving Generative Adversarial Networks with Image Quality Assessment
, 2021 The research to find new ways to improve Generative Adversarial Networks (GANs) and ways to evaluate the data they produce is quite active. However, approaches to directly using those evaluation steps to improve Generative Adversarial Networks are quite ...
Perkins-Ollila, Justin W.
core
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 +5 more
wiley +1 more source
KG-GAN: Knowledge-Guided Generative Adversarial Networks
CoRR, 2019 Can generative adversarial networks (GANs) generate roses of various colors given only roses of red petals as input? The answer is negative, since GANs' discriminator would reject all roses of unseen petal colors. In this study, we propose knowledge-guided GAN (KG-GAN) to fuse domain knowledge with the GAN framework.
Che-Han Chang +3 more
openaire +2 more sources
Exploring the Role of Recursive Convolutional Layer in Generative Adversarial Networks
, 2023 This paper aims to study the potentialities of incorporating recursive layers into Generative Adversarial Networks (GANs). Drawing inspiration from biological systems, in which feedback connections are prevalent, different studies investigated their ...
Hagenbuchner, Markus +4 more
core +1 more source
Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Advanced Intelligent Discovery, EarlyView.Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source