Uncertainty in Model-Agnostic Meta-Learning using Variational Inference

Thanh-Toan Do University of Liverpool thanh-toan.do@liverpool.ac.uk Abstract W e introduce a new, rigorously-formulated Bayesian meta-learning algorithm that learns a probability distribution of model parameter prior for few-shot learning. The proposed algorithm employs a gradient-based variational inference to infer the posterior of model parameters to a new task. Our algorithm can be applied to any model architecture and can be implemented in various machine learning paradigms, including regression and classification. W e show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate, with state-of-the-art calibration and classification results on two few-shot classification benchmarks (Omniglot and Mini-ImageNet), and competitive results in a multi-modal task-distribution regression. 1. Introduction Machine learning, in particular deep learning, has thrived during the last decade, producing results that were previously considered to be infeasible in several areas. For instance, outstanding results have been achieved in speech and image understanding [1-4], and medical image analysis [5]. However, the development of these machine learning methods typically requires a large number of training samples to achieve notable performance. Such requirement contrasts with the human ability of quickly adapting to new learning tasks using few "training" samples. This difference may be due to the fact that humans tend to exploit prior knowledge to facilitate the learning of new tasks, while machine learning algorithms often do not use any prior knowledge (e.g., training from scratch with random initialisation) [6] or rely on weak prior knowledge to learn new tasks (e.g., training from pre-trained models) [7]. This challenge has motivated the design of machine learning methods that can make more effective use of prior knowledge to adapt to new learning tasks using few training samples [8].