  Cross-domain few-shot learning (CDFSL) addresses learning problems where
knowledge needs to be transferred from one or more source domains into an
instance-scarce target domain with an explicitly different distribution.
Recently published CDFSL methods generally construct a universal model that
combines knowledge of multiple source domains into one feature extractor. This
enables efficient inference but necessitates re-computation of the extractor
whenever a new source domain is added. Some of these methods are also
incompatible with heterogeneous source domain extractor architectures. We
propose feature extractor stacking (FES), a new CDFSL method for combining
information from a collection of extractors, that can utilise heterogeneous
pretrained extractors out of the box and does not maintain a universal model
that needs to be re-computed when its extractor collection is updated. We
present the basic FES algorithm, which is inspired by the classic stacked
generalisation approach, and also introduce two variants: convolutional FES
(ConFES) and regularised FES (ReFES). Given a target-domain task, these
algorithms fine-tune each extractor independently, use cross-validation to
extract training data for stacked generalisation from the support set, and
learn a simple linear stacking classifier from this data. We evaluate our FES
methods on the well-known Meta-Dataset benchmark, targeting image
classification with convolutional neural networks, and show that they can
achieve state-of-the-art performance.
