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Modern ML methods excel when training data is IID, large-scale, and welllabeled. Learning in less ideal conditions remains an open challenge. Thesub-fields of few-shot, continual, transfer, and representation learning havemade substantial strides in learning under adverse conditions; each affordingdistinct advantages through methods and insights. These methods addressdifferent challenges such as data arriving sequentially or scarce trainingexamples, however often the difficult conditions an ML system will face overits lifetime cannot be anticipated prior to deployment. Therefore, general MLsystems which can handle the many challenges of learning in practical settingsare needed. To foster research towards the goal of general ML methods, weintroduce a new unified evaluation framework - FLUID (Flexible SequentialData). FLUID integrates the objectives of few-shot, continual, transfer, andrepresentation learning while enabling comparison and integration of techniquesacross these subfields. In FLUID, a learner faces a stream of data and mustmake sequential predictions while choosing how to update itself, adapt quicklyto novel classes, and deal with changing data distributions; while accountingfor the total amount of compute. We conduct experiments on a broad set ofmethods which shed new insight on the advantages and limitations of currentsolutions and indicate new research problems to solve. As a starting pointtowards more general methods, we present two new baselines which outperformother evaluated methods on FLUID. Project page:https://raivn.cs.washington.edu/projects/FLUID/.

FLUID: A Unified Evaluation Framework for Flexible Sequential Data