Sequoia: A Software Framework to Unify Continual Learning Research

Understand

The field of Continual Learning (CL) seeks to develop algorithms that accumulate knowledge and skills over time through interaction with non-stationary environments.

• In practice, a plethora of evaluation procedures (settings) and algorithmic solutions (methods) exist, each with their own potentially disjoint set of assumptions.
• This variety makes measuring progress in CL difficult.
• We propose a taxonomy of settings, where each setting is described as a set of assumptions.