Bayesian Few-Shot Classification with One-vs-Each P\'olya-Gamma Augmented Gaussian Processes

• URL: http://arxiv.org/abs/2007.10417v2
• Date: Thu, 21 Jan 2021 19:54:57 GMT
• Title: Bayesian Few-Shot Classification with One-vs-Each P\'olya-Gamma Augmented Gaussian Processes
• Authors: Jake Snell, Richard Zemel
• Abstract summary: Few-shot classification (FSC) is an important step on the path toward human-like machine learning. We propose a novel combination of P'olya-Gamma augmentation and the one-vs-each softmax approximation that allows us to efficiently marginalize over functions rather than model parameters. We demonstrate improved accuracy and uncertainty quantification on both standard few-shot classification benchmarks and few-shot domain transfer tasks.
• Score: 7.6146285961466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot classification (FSC), the task of adapting a classifier to unseen classes given a small labeled dataset, is an important step on the path toward human-like machine learning. Bayesian methods are well-suited to tackling the fundamental issue of overfitting in the few-shot scenario because they allow practitioners to specify prior beliefs and update those beliefs in light of observed data. Contemporary approaches to Bayesian few-shot classification maintain a posterior distribution over model parameters, which is slow and requires storage that scales with model size. Instead, we propose a Gaussian process classifier based on a novel combination of P\'olya-Gamma augmentation and the one-vs-each softmax approximation that allows us to efficiently marginalize over functions rather than model parameters. We demonstrate improved accuracy and uncertainty quantification on both standard few-shot classification benchmarks and few-shot domain transfer tasks.

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