# Task Agnostic Continual Learning via Meta Learning

Source: [Task Agnostic Continual Learning via Meta Learning](<https://www.sota2.com/research/paper/68fa65e31e209131cea0d2f4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:58:47.394Z

Authors: Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A. Rusu, Yee Whye Teh, Razvan Pascanu

Published: 2019-06-12T15:17:47.000Z

## Abstract

While neural networks are powerful function approximators, they suffer from catastrophic forgetting when the data distribution is not stationary. One particular formalism that studies learning under non-stationary distribution is provided by continual learning, where the non-stationarity is imposed by a sequence of distinct tasks. Most methods in this space assume, however, the knowledge of task boundaries, and focus on alleviating catastrophic forgetting. In this work, we depart from this view and move the focus towards faster remembering -- i.e measuring how quickly the network recovers performance rather than measuring the network's performance without any adaptation. We argue that in many settings this can be more effective and that it opens the door to combining meta-learning and continual learning techniques, leveraging their complementary advantages. We propose a framework specific for the scenario where no information about task boundaries or task identity is given. It relies on a separation of concerns into what task is being solved and how the task should be solved. This framework is implemented by differentiating task specific parameters from task agnostic parameters, where the latter are optimized in a continual meta learning fashion, without access to multiple tasks at the same time. We showcase this framework in a supervised learning scenario and discuss the implication of the proposed formalism.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Online Learning on Omniglot-MNIST-FashionMNIST (p = 0.98)](<https://www.sota2.com/research/sota/online-learning-on-omniglot-mnist-fashionmnist-p-0-98.md>): Online Learning; Omniglot-MNIST-FashionMNIST (p = 0.98); Cumulative Online Accuracy: 0.911.

- [Online Learning on Omniglot-MNIST-FashionMNIST p = 0.9](<https://www.sota2.com/research/sota/online-learning-on-omniglot-mnist-fashionmnist-p-0-9.md>): Online Learning; Omniglot-MNIST-FashionMNIST p = 0.9; Cumulative Online Accuracy: 74.8.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1906.05201>)

[PDF](<https://arxiv.org/pdf/1906.05201.pdf>)