Results 91 to 100 of about 2,921,969 (298)
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 +3 more
openaire +2 more sources
Ovarian Cancer Diagnosis and Chemoresistance Prediction Model Based on cfRNA Molecular Signature
Advanced Science, EarlyView.A deep learning model analyzes cfRNA profiles extracted from the blood of OVCA patients. This innovative approach distinguishes OVCA from healthy controls with high accuracy. Crucially, it reliably predicts patient response to chemotherapy (sensitive versus resistant subgroups).
Qinhao Guo +14 more
wiley +1 more source
Advanced Science, EarlyView.Eligibility flow and real‐world AMD burden in the UKB retinal imaging cohort and TMUEH external‐validation cohort. Overview of the ORBIT‐AMD architecture, integrating retinal representation pretraining, bilateral eye‐graph modeling and concept bottleneck learning to support ordered risk, bilateral context, interpretable lesion concepts, longitudinal ...
Xuehao Cui +3 more
wiley +1 more source
Generative Adversarial Networks for Lupus Diagnostics
, 2019 The recent boom of Machine Learning Network Architectures like Generative Adversarial Networks (GAN), Deep Convolution Generative Adversarial Networks (DCGAN), Self Attention Generative Adversarial Networks (SAGAN), Context Conditional Generative ...
Periasamy, Pradeep
core +3 more sources
Conditional Generative Adversarial Network for Monocular Image Depth Map Prediction
, 2023 Deep map prediction plays a crucial role in comprehending the three-dimensional structure of a scene, which is essential for enabling mobile robots to navigate autonomously and avoid obstacles in complex environments.
Zheng Zhang +3 more
core +1 more source
A PRIMER ON GENERATIVE ADVERSARIAL NETWORKS
International Journal of Innovative Research in Computer Science & Technology, 2020 Generative Adversarial Networks (GANs) is a type of deep neural network architecture that utilizes unsupervised machine learning to generate data. They were presented in 2014, in a paper by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This paper will introduce the core components of GANs.
Dr. Vikas Thada +4 more
openaire +1 more source
StackingNet: Collective Inference Across Independent AI Foundation Models
Advanced Science, EarlyView.ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li +4 more
wiley +1 more source
Coevolution of Generative Adversarial Networks [PDF]
, 2019 Published in EvoApplications ...
Victor Costa +2 more
openaire +3 more sources
Time Resolved DNA Barcodes for Information Encoding and Dynamic Encryption
Advanced Science, EarlyView.This study establishes a molecular information platform based on DNA Temporal Barcodes. Information is encoded through combinations of DNA tags with distinct retention times, while dynamic encryption is achieved through a key‐triggered DNA ligation.
Likang Chu +7 more
wiley +1 more source
Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC) [PDF]
, 2017 Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over ...
Gabriel L., Guimaraes +3 more
core +1 more source