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

By Jake Snell, Richard S. Zemel. 2020

68 citations recorded by the source corpus.

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 Polya-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.

Subfields: Artificial Intelligence

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1. Steinar Laenen
2. Luca Bertinetto

Citations in the corpus

• DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning (2020, unknown): “, [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], target at effectively”
• On Episodes, Prototypical Networks, and Few-shot Learning (2020, methodology): “Finally, two recent meta-learning approaches based on Gaussian Processes [33, 46] also”