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GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
Idan Achituve · Aviv Navon · Yochai Yemini · Gal Chechik · Ethan Fetaya

Thu Jul 22 09:00 PM -- 11:00 PM (PDT) @ Virtual #None

Gaussian processes (GPs) are non-parametric, flexible, models that work well in many tasks. Combining GPs with deep learning methods via deep kernel learning (DKL) is especially compelling due to the strong representational power induced by the network. However, inference in GPs, whether with or without DKL, can be computationally challenging on large datasets. Here, we propose GP-Tree, a novel method for multi-class classification with Gaussian processes and DKL. We develop a tree-based hierarchical model in which each internal node of the tree fits a GP to the data using the P├│lya-Gamma augmentation scheme. As a result, our method scales well with both the number of classes and data size. We demonstrate the effectiveness of our method against other Gaussian process training baselines, and we show how our general GP approach achieves improved accuracy on standard incremental few-shot learning benchmarks.

Author Information

Idan Achituve (Bar-Ilan)
Aviv Navon (Bar-Ilan University)
Yochai Yemini (Bar-Ilan University)
Gal Chechik (NVIDIA / Bar-Ilan University)
Ethan Fetaya (Bar-Ilan University)

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