Continuous Meta-Learning without Tasks
Meta-learning is a promising strategy for learning to efficiently learn within new tasks, using data gathered from a distribution of tasks.However, the meta-learning literature thus far has focused on the task segmented setting, where at train-time, offline data is assumed to be split according to the underlying task, and at test-time, the algorithms are optimized to learn in a single task.In this work, we enable the application of generic meta-learning algorithms to settings where this task segmentation is unavailable, such as continual online learning with a time-varying task.We present meta-learning via online changepoint analysis, an approach which augments a meta-learning algorithm with a differentiable Bayesian changepoint detection scheme.The framework allows both training and testing directly on time series data without segmenting it into discrete tasks.We demonstrate the utility of this approach on a nonlinear meta-regression benchmark as well as two meta-image-classification benchmarks.
Bayesian changepoint detection enables meta-learning directly from time series data.The paper considers the meta-learning in the task un-segmented setting and apply Bayesian online change point detection with meta-learning.This paper pushes meta-learning towards task-unsegmented settings, where the MOCA framework adopts a Bayesian changepoint estimation scheme for task change detection.