A Conditional Deep Framework for Automatic Layout Generation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3198686
Abstract: Automatic layout generation, which means making computers enjoy creativity, is difficult yet exciting work. Up to now, how to generate reasonable and visually appealing layouts remains a complex challenge. In this paper, we propose a… read more here.
Keywords: layout; automatic layout; conditional deep; layout generation ... See more keywords
Experienced Deep Reinforcement Learning With Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Communications"
DOI: 10.1109/tcomm.2020.3031930
Abstract: In this paper, a novel experienced deep reinforcement learning (deep-RL) framework is proposed to provide model-free resource allocation for ultra reliable low latency communication (URLLC-6G) in the downlink of a wireless network. The goal is… read more here.
Keywords: deep reinforcement; experienced deep; deep framework; latency ... See more keywords
RetinexDIP: A Unified Deep Framework for Low-Light Image Enhancement
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2021.3073371
Abstract: Low-light images suffer from low contrast and unclear details, which not only reduces the available information for humans but limits the application of computer vision algorithms. Among the existing enhancement techniques, Retinex-based and learning-based methods… read more here.
Keywords: light image; unified deep; low light; image enhancement ... See more keywords