Difference between revisions of "Conditional Adversarial Architecture (CAA)"
| Line 12: | Line 12: | ||
<youtube>425rPG580dQ</youtube> | <youtube>425rPG580dQ</youtube> | ||
<youtube>l_NA911jMZs</youtube> | <youtube>l_NA911jMZs</youtube> | ||
| − | <youtube> | + | <youtube>hY9Bc3mtphs</youtube> |
Revision as of 22:45, 17 October 2018
- Image-to-image translation with conditional adversarial networks | Isola et al.
- Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture | Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi Jaakkola, Matt Bianchi - Massachusetts Institute of Technology (MIT) & Massachusetts General Hospital
- Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery | Polykovskiy D, Zhebrak A, Vetrov D, Ivanenkov Y, Aladinskiy V, Mamoshina P, Bozdaganyan M, Aliper A, Zhavoronkov A, Kadurin A
Occlusion is a fundamental problem in human pose estimation and many other vision tasks. Instead of hallucinating missing body parts based on visible ones, we demonstrate a solution that leverages radio signals to accurately track the 2D human pose through walls and obstructions. Through-Wall Human Pose Estimation Using Radio Signals | Mingmin Zhao, Tianhong Li, Mohammad Abu, Alsheikh Yonglong, Tian Hang Zhao, Antonio Torralba, Dina Katabi - MIT CSAIL