no code implementations • 30 Oct 2023 • Siyu Qi, Achintha Wijesinghe, Lahiru D. Chamain, Zhi Ding
Our goal is to optimize DL models such that the encoder latent requires low channel bandwidth while still delivers feature information for high classification accuracy.
no code implementations • 10 Aug 2023 • Siyu Qi, Lahiru D. Chamain, Zhi Ding
Among major deep learning (DL) applications, distributed learning involving image classification require effective image compression codecs deployed on low-cost sensing devices for efficient transmission and storage.
no code implementations • 6 Mar 2021 • Lahiru D. Chamain, Fabien Racapé, Jean Bégaint, Akshay Pushparaja, Simon Feltman
An increasing share of captured images and videos are transmitted for storage and remote analysis by computer vision algorithms, rather than to be viewed by humans.
no code implementations • 10 Nov 2020 • Lahiru D. Chamain, Fabien Racapé, Jean Bégaint, Akshay Pushparaja, Simon Feltman
An increasing share of image and video content is analyzed by machines rather than viewed by humans, and therefore it becomes relevant to optimize codecs for such applications where the analysis is performed remotely.
no code implementations • 4 Sep 2019 • Lahiru D. Chamain, Zhi Ding
Furthermore, we show that traditional augmentation transforms such as flipping/shifting are ineffective in the DWT domain and present different augmentation transformations to achieve more accurate classification without any additional cost.