@inproceedings{046ef3c0afbd49e586c9d4f779235e3b,
title = "CLIPS: Continual Learning Infrastructure for Plastics Sorting",
abstract = "Plastics detection using mobile apps can greatly assist in plastics sorting at the source and allow great improvements in the percentage of plastics that are recycled. Previous work such as DeepWaste and MWaste has tackled general waste classification, including plastics, but few efforts focus on real-time plastic type identification on mobile devices. CLIPS addresses this gap by developing a mobile app in combination with a cloud service that enables plastic-material classification. Further, CLIPS utilizes a continual learning architecture to adapt the model to the local stream of plastics, allowing for greater detection accuracy over time. We demonstrate that our approach can improve performance by 37\% through continual learning compared to a pre-trained model. We also demonstrate a positive backward transfer of +27\% and a forward transfer of +19.63\% with continual learning over time.",
keywords = "Continual learning, MobileNet, deep learning, mobile applications, plastic classification, recycling, sustainability",
author = "Shivm Mehta and Vaishali Maheshkar and Charuvahan Adhivarahan and Karthik Dantu",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 24th International Conference on Machine Learning and Applications, ICMLA 2025 ; Conference date: 03-12-2025 Through 05-12-2025",
year = "2025",
doi = "10.1109/ICMLA66185.2025.00183",
language = "English",
series = "Proceedings - 2025 24th International Conference on Machine Learning and Applications, ICMLA 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1197--1204",
editor = "Wani, \{M. Arif\} and Khoshgoftaar, \{Taghi M.\} and Huanjing Wang and Kehan Gao and Safak Kayikci",
booktitle = "Proceedings - 2025 24th International Conference on Machine Learning and Applications, ICMLA 2025",
address = "United States",
}