Cross-domain Few-shot Meta-learning Using Stacking

Hongyu Wang, Eibe Frank, Bernhard Pfahringer, Michael Mayo, Geoffrey Holmes

Abstract: Cross-domain few-shot meta-learning (CDFSML) addresses learning problems where knowledge needs to be transferred from several source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSML methods generally construct a "universal model" that combines knowledge of multiple source domains into one backbone feature extractor. This enables efficient inference but necessitates re-computation of the backbone whenever a new source domain is added. Moreover, these methods often derive their universal model from a collection of backbones -- normally one for each source domain -- where these backbones are constrained to have the same architecture as the universal model. We propose feature extractor stacking (FES), a new CDFSML method for combining information from a collection of backbones that imposes no constraints on the backbones' architecture and does not require re-computing a universal model when a backbone for a new source domain becomes available. We present the basic FES algorithm, which is inspired by the classic stacking approach to meta-learning, and also introduce two variants: convolutional FES (ConFES) and regularised FES (ReFES). Given a target-domain task, these algorithms fine-tune each backbone independently, use cross-validation to extract meta training data from the support set available for the task, and learn a simple linear meta-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.