ALMA (On Anytime Learning At Macroscale) is a new framework, where like (offline) Continual Learning, data arrive sequentially in large (mega)batches over time. See paper https://arxiv.org/abs/2106.09563. Unlike CL however, we do not assume that there is a shift in the underlying distribution. Rather, the goal of ALMA is develop strategies that perform well troughout the learning experience (not just at the end), and that do so efficiently from a compute and memory perspective. ALMA explore a different line of questions arising in this setting, namely :
How long should a model wait and aggregate data before training again ?
Should the model increase its capacity over time to account for the additional data ?
ALMA is a different framing of InstanceIncremental therefore we focus on this scenario.
Class and Instances Incremental scenarios are proposed in the scenario from the original paper (next section).
from continuum.scenarios import ALMA scenario = ALMA(your_dataset, nb_megabatches=50)