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 2018https://arxiv.org/abs/1711.09601[3] Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, Tinne Tuytelaars, Memory Aware Synapses: Learningwhat(not) to forget, ECCV 2018https://arxiv.org/abs/1711.09601[4]Sayna 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​​​