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

我们提出了一种名为 GP-Tree 的新方法，通过树状层次模型进行多类分类，该模型使用高斯过程和 DKL，其推断能力对于大规模数据集十分具有强大的表征能力，我们证明了该方法在标准增量少样本学习基准测试中的效率和准确性高于其他基线。

GP-Tree: 一种用于少样本增量学习的高斯过程分类器