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MetaFun: Meta-Learning with Iterative Functional Updates
Jin Xu · Jean-Francois Ton · Hyunjik Kim · Adam Kosiorek · Yee-Whye Teh

Wed Jul 15 03:00 PM -- 03:45 PM & Thu Jul 16 02:00 AM -- 02:45 AM (PDT) @ Virtual

We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a finite-dimensional one. Furthermore, rather than directly producing the representation, we learn a neural update rule resembling functional gradient descent which iteratively improves the representation. The final representation is used to condition the decoder to make predictions on unlabeled data. Our approach is the first to demonstrates the success of encoder-decoder style meta-learning methods like conditional neural processes on large-scale few-shot classification benchmarks such as miniImageNet and tieredImageNet, where it achieves state-of-the-art performance.

Author Information

Jin Xu (University of Oxford)
Jean-Francois Ton (University of Oxford)
Hyunjik Kim (DeepMind)
Adam Kosiorek (DeepMind)
Yee-Whye Teh (Oxford and DeepMind)

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