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 Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach,MohamedElhoseiny, Efficient Lifelong Learning with A-GEM, ICLR, 2019 Mohamed Elhoseiny,Francesca Babiloni, Rahaf Aljundi, ManoharPaluri,Marcus Rohrbach, Tinne Tuytelaars, Exploring the Challenges towards Lifelong Fact Learning, ACCV 2018https://arxiv.org/abs/1711.09601 Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, Tinne Tuytelaars, Memory Aware Synapses: Learningwhat(not) to forget, ECCV 2018https://arxiv.org/abs/1711.09601Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, MarcusRohrbachUncertainty-guided Continual Learning with Bayesian Neural Networks https://arxiv.org/abs/1906.02425For more references, you may visit https://nips.cc/Conferences/2018/Schedule?showEvent=10910 https://icml.cc/Conferences/2019/Schedule?showEvent=3528
Program - Computer Science
Division - Computer, Electrical and Mathematical Sciences and Engineering
Center Affiliation - Visual Computing Center
Field of Study - Computer Vision and Machine Learning
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.