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The ability to learn from a small number of examples has been a difficultproblem in machine learning since its inception. While methods have succeededwith large amounts of training data, research has been underway in how toaccomplish similar performance with fewer examples, known as one-shot or moregenerally few-shot learning. This technique has been shown to have promisingperformance, but in practice requires fixed-size inputs making it impracticalfor production systems where class sizes can vary. This impedes training andthe final utility of few-shot learning systems. This paper describes anapproach to constructing and training a network that can handle arbitraryexample sizes dynamically as the system is used.

Dynamic Input Structure and Network Assembly for Few-Shot Learning