Multi-Stream Cellular Test-Time Adaptation of Real-Time Models Evolving in Dynamic Environments

B Gérin, A Halin, A Cioppa, M Henry… - Proceedings of the …, 2024 - openaccess.thecvf.com
In the era of the Internet of Things (IoT) objects connect through a dynamic network
empowered by technologies like 5G enabling real-time data sharing. However smart objects …

DaCapo: Accelerating Continuous Learning in Autonomous Systems for Video Analytics

Y Kim, C Oh, J Hwang, W Kim, S Oh, Y Lee… - arXiv preprint arXiv …, 2024 - arxiv.org
Deep neural network (DNN) video analytics is crucial for autonomous systems such as self-
driving vehicles, unmanned aerial vehicles (UAVs), and security robots. However, real-world …

On the Query Strategies for Efficient Online Active Distillation

M Boldo, E Martini, M De Marchi, S Aldegheri… - arXiv preprint arXiv …, 2023 - arxiv.org
Deep Learning (DL) requires lots of time and data, resulting in high computational demands.
Recently, researchers employ Active Learning (AL) and online distillation to enhance …

Master Thesis: Online Distillation with Continual Learning for Cyclic Domain Shifts

J Houyon - 2023 - matheo.uliege.be
The technique of online distillation has become increasingly popular in adapting real-time
deep neural networks using a slow and accurate teacher model. However, one of the most …

[PDF][PDF] DACAPO: Accelerating Continuous Learning in Autonomous Systems for Video Analytics

YKCOJ Hwang, WKS Oh, YLH Sharma… - jongse-park.github.io
Deep neural network (DNN) video analytics is crucial for autonomous systems such as self-
driving vehicles, unmanned aerial vehicles (UAVs), and security robots. However, real-world …