Continual learning aims to
learn new tasks without forgetting previously learned ones. This is especially
challenging when one cannot access data from
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, Mohamed Elhoseiny, Efficient
Lifelong Learning with A-GEM, ICLR, 2019
[2] Mohamed Elhoseiny,Francesca Babiloni, Rahaf
Aljundi, Manohar Paluri, Marcus
Rohrbach, Tinne Tuytelaars, Exploring the Challenges towards Lifelong Fact
Learning, ACCV 2018
https://arxiv.org/abs/1711.09601
[3] Rahaf Aljundi, Francesca Babiloni, Mohamed
Elhoseiny, Marcus Rohrbach, Tinne Tuytelaars, Memory Aware Synapses: Learning what (not) to forget, ECCV 2018
https://arxiv.org/abs/1711.09601
[4]Sayna
Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach Uncertainty-guided Continual Learning with Bayesian
Neural Networks https://arxiv.org/abs/1906.02425
For more references, you may visit
https://nips.cc/Conferences/2018/Schedule?showEvent=10910
https://icml.cc/Conferences/2019/Schedule?showEvent=3528