Results 61 to 70 of about 43,961 (294)
Generative Adversarial Networks: Recent Developments [PDF]
10 ...
Zamorski, Maciej +3 more
openaire +2 more sources
Multi-agent Diverse Generative Adversarial Networks [PDF]
This is an updated version of our CVPR'18 paper with the same title.
Arnab Ghosh +4 more
openaire +2 more sources
Continual Learning for Multimodal Data Fusion of a Soft Gripper
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley +1 more source
Prediction Method of Multiple Related Time Series Based on Generative Adversarial Networks [PDF]
Weijie Wu +4 more
openalex +1 more source
CellPolaris decodes how transcription factors guide cell fate by building gene regulatory networks from transcriptomic data using transfer learning. It generates tissue‐ and cell‐type‐specific networks, identifies master regulators in cell state transitions, and simulates TF perturbations in developmental processes.
Guihai Feng +27 more
wiley +1 more source
This study presents a novel microscopic imaging system capable of rapid, section‐free scanning of irregular tissue surfaces, delivering high sensitivity for detecting cancer cell clusters during intraoperative tumor margin assessment. Abstract Rapid and accurate intraoperative examination of tumor margins is crucial for precise surgical treatment, yet ...
Zhicheng Shao +17 more
wiley +1 more source
Remote Sensing Image Dataset Expansion Based on Generative Adversarial Networks with Modified Shuffle Attention [PDF]
Chen Lü, Hongjun Wang, Xianghao Meng
openalex +1 more source
Inverting the Generator of a Generative Adversarial Network [PDF]
Under review at IEEE ...
Antonia Creswell, Anil Anthony Bharath
openaire +7 more sources
Unpaired Learning‐Enabled Nanotube Identification from AFM Images
Identifying nanotubes on rough substrates is notoriously challenging for conventional image analysis. This work presents an unpaired deep learning approach that automatically extracts nanotube networks from atomic force microscopy images, even on complex polymeric surfaces used in roll‐to‐roll printing.
Soyoung Na +10 more
wiley +1 more source
Domain Transferred Image Recognition via Generative Adversarial Network [PDF]
Haoqi Hu +3 more
openalex +1 more source

