  Modern ML methods excel when training data is IID, large-scale, and well
labeled. Learning in less ideal conditions remains an open challenge. The
sub-fields of few-shot, continual, transfer, and representation learning have
made substantial strides in learning under adverse conditions; each affording
distinct advantages through methods and insights. These methods address
different challenges such as data arriving sequentially or scarce training
examples, however often the difficult conditions an ML system will face over
its lifetime cannot be anticipated prior to deployment. Therefore, general ML
systems which can handle the many challenges of learning in practical settings
are needed. To foster research towards the goal of general ML methods, we
introduce a new unified evaluation framework - FLUID (Flexible Sequential
Data). FLUID integrates the objectives of few-shot, continual, transfer, and
representation learning while enabling comparison and integration of techniques
across these subfields. In FLUID, a learner faces a stream of data and must
make sequential predictions while choosing how to update itself, adapt quickly
to novel classes, and deal with changing data distributions; while accounting
for the total amount of compute. We conduct experiments on a broad set of
methods which shed new insight on the advantages and limitations of current
solutions and indicate new research problems to solve. As a starting point
towards more general methods, we present two new baselines which outperform
other evaluated methods on FLUID. Project page:
https://raivn.cs.washington.edu/projects/FLUID/.
