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Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
Massimiliano Patacchiola · Jack Turner · Elliot Crowley · Michael O'Boyle · Amos Storkey

Wed Dec 09 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Continual/Meta/Misc Learning

Recently, different machine learning methods have been introduced to tackle the challenging few-shot learning scenario that is, learning from a small labeled dataset related to a specific task. Common approaches have taken the form of meta-learning: learning to learn on the new problem given the old. Following the recognition that meta-learning is implementing learning in a multi-level model, we present a Bayesian treatment for the meta-learning inner loop through the use of deep kernels. As a result we can learn a kernel that transfers to new tasks; we call this Deep Kernel Transfer (DKT). This approach has many advantages: is straightforward to implement as a single optimizer, provides uncertainty quantification, and does not require estimation of task-specific parameters. We empirically demonstrate that DKT outperforms several state-of-the-art algorithms in few-shot classification, and is the state of the art for cross-domain adaptation and regression. We conclude that complex meta-learning routines can be replaced by a simpler Bayesian model without loss of accuracy.

Author Information

Massimiliano Patacchiola (University of Edinburgh)

Massimiliano is a postdoctoral researcher at the University of Edinburgh in the Machine Learning group - School of Informatics. He is interested in efficient learning (few-shot, self-supervised, meta-learning), Bayesian methods (Gaussian processes), and (when in the right mood) robotics. Previously he has been an intern at Snapchat and a PhD student at the University of Plymouth.

Jack Turner (University of Edinburgh)
Elliot Crowley (University of Edinburgh)
Michael O'Boyle (University of Edinburgh)
Amos Storkey (University of Edinburgh)

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