TY - JOUR A1 - Lavda, Frantzeska A1 - Ramapuram, Jason A1 - Gregorová, Magda A1 - Kalousis, Alexandros T1 - Continual Classification Learning Using Generative Models T2 - CoRR N2 - Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences. Neural networks can learn multiple tasks when trained on them jointly, but cannot maintain performance on previously learned tasks when tasks are presented one at a time. This problem is called catastrophic forgetting. In this work, we propose a classification model that learns continuously from sequentially observed tasks, while preventing catastrophic forgetting. We build on the lifelong generative capabilities of [10] and extend it to the classification setting by deriving a new variational bound on the joint log likelihood, $\log p(x; y)$. Y1 - 2018 UR - https://opus4.kobv.de/opus4-fhws/frontdoor/index/index/docId/4966 VL - abs/1810.10612 ER -