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arXiv · 2003.02455

PAC-Bayes meta-learning with implicit task-specific posteriors

Cuong Nguyen·Thanh-Toan Do·Gustavo Carneiro
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Abstract

We introduce a new and rigorously-formulated PAC-Bayes meta-learning algorithm that solves few-shot learning. Our proposed method extends the PAC-Bayes framework from a single task setting to the meta-learning multiple task setting to upper-bound the error evaluated on any, even unseen, tasks and samples. We also propose a generative-based approach to estimate the posterior of task-specific model parameters more expressively compared to the usual assumption based on a multivariate normal distribution with a diagonal covariance matrix. We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate, with state-of-the-art calibration and classification results on few-shot classification (mini-ImageNet and tiered-ImageNet) and regression (multi-modal task-distribution regression) benchmarks.

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Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro. 2020-03-05. PAC-Bayes meta-learning with implicit task-specific posteriors. https://arxiv.org/abs/2003.02455

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