Bayesian Meta Learning for the Few Shot Setting via Deep Kernels
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.
NeurIPS