Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian ProcessesDownload PDF

Sep 28, 2020 (edited Mar 18, 2021)ICLR 2021 PosterReaders: Everyone
  • Keywords: few-shot learning, gaussian processes, bayesian deep learning, uncertainty estimation
  • 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 Pólya-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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  • One-sentence Summary: We propose a Gaussian process approach to few-shot classification based on the one-vs-each softmax approximation and Pólya-Gamma augmentation, and demonstrate competitive few-shot accuracy and strong uncertainty quantification.
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