Continual Learning


Project Description

Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot accessdatafrom previous tasks and when the model has a fixed capacity. In this project, the goal is to develop and improve the capability of the machine learning methods not to forget older concepts as time passes.References[1] Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach,MohamedElhoseiny, Efficient Lifelong Learning with A-GEM, ICLR, 2019[2] Mohamed Elhoseiny,Francesca Babiloni, Rahaf Aljundi, ManoharPaluri,Marcus Rohrbach, Tinne Tuytelaars, Exploring the Challenges towards Lifelong Fact Learning, ACCV 2018[3] Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, Tinne Tuytelaars, Memory Aware Synapses: Learningwhat(not) to forget, ECCV 2018[4]Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, MarcusRohrbachUncertainty-guided Continual Learning with Bayesian Neural Networks more references, you may visit​​​
Program - Computer Science
Division - Computer, Electrical and Mathematical Sciences and Engineering
Faculty Lab Link - continual-learning
Center Affiliation - Visual Computing Center
Field of Study - ​Computer Vision and Machine Learning

About the

Mohamed Elhoseiny

Mohamed Elhoseiny

Desired Project Deliverables

​Develop a working research prototype for a continual learning approach1) students should learn about machine learning, deep learning, and the respective target application chosen for the internship. 2) students are expected to show capability to go from an idea to a working prototype; pushing the limits of what the state of the art can do.​