Recent advances have enabled diffusion models to efficiently compress videos while maintaining high visual quality. By storing only keyframes and using these models to interpolate frames during playback, this method ensures high fidelity with minimal data. The process is adaptive, balancing detail retention and compression ratio, and can be conditioned on lightweight information like text descriptions or edge maps for improved results.
Diffusion Based Video Compression
Soft Actor Critic with Inhibitory Networks for Retraining UAV Controllers Faster
Minkyu Choi, Max Filter, Kevin, Alcedo, Thayne T. Walker, David Rosenbluth, and Jaime S. Ide.
International Conference on Unmanned Aircraft Systems (ICUAS), 2022
The rapid evolution in autonomous unmanned aerial vehicles (UAVs) technology has spurred significant advancements in their control systems. A prominent challenge in this domain is balancing the agility of Proportional-Integral-Derivative (PID) systems for low-level control with the adaptability of Deep Reinforcement Learning (DRL) for navigation through complex environments. This post delves into a novel approach that combines these technologies to improve the retraining efficiency of UAV controllers using Soft Actor-Critic (SAC) with inhibitory networks.