大规模非平稳任务分布下较少遗忘的元学习

Zhenyi Wang, Li Shen, Le Fang, Qiuling Suo, Dongling Zhan, Tiehang Duan, Mingchen Gao
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引用次数: 7

摘要

. 机器智能的范式从纯粹的监督学习转向更实际的场景,当许多松散相关的未标记数据可用而标记数据稀缺时。大多数现有算法都假定底层任务分布是平稳的。在这里,我们考虑一个更现实和更具挑战性的设置,即任务分布随着时间的推移而变化。我们将这个问题命名为S -半监督元学习,其中E包含S个子集,缩写为set。在这种更现实的环境中出现了两个关键挑战:(i)如何在存在大量未标记的分布外(OOD)数据的情况下使用未标记数据;(ii)如何防止由于任务分布的转移而导致的对先前学习的任务分布的灾难性遗忘。为了解决这两个主要的挑战,我们提出了一种基于知识的半超学习型元学习方法(ORDER)。具体来说,我们的ORDER引入了一种新的互信息正则化来对未标记的OOD数据模型进行鲁棒化,并采用最优传输正则化来记住特征空间中先前学习的知识。此外,我们在一个非常具有挑战性的数据集上测试了我们的方法:set在由(至少)72K个任务组成的大规模非平稳半监督任务分布上。通过大量的实验,我们证明了所提出的ORDER减轻了对不断变化的任务分布的遗忘,并且比相关的强基线对OOD数据更具鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions
. The paradigm of machine intelligence moves from purely supervised learning to a more practical scenario when many loosely related unlabeled data are available and labeled data is scarce. Most existing algo-rithms assume that the underlying task distribution is stationary. Here we consider a more realistic and challenging setting in that task distributions evolve over time. We name this problem as S emi-supervised meta-learning with E volving T ask di S tributions, abbreviated as SETS . Two key challenges arise in this more realistic setting: (i) how to use unlabeled data in the presence of a large amount of unlabeled out-of-distribution (OOD) data; and (ii) how to prevent catastrophic forgetting on previously learned task distributions due to the task distribution shift. We propose an O OD R obust and knowle D ge pres E rved semi-supe R vised meta-learning approach ( ORDER ) ‡ , to tackle these two major challenges. Specifically, our ORDER introduces a novel mutual information regularization to robustify the model with unlabeled OOD data and adopts an optimal transport regularization to remember previously learned knowledge in feature space. In addition, we test our method on a very challenging dataset: SETS on large-scale non-stationary semi-supervised task distributions consisting of (at least) 72K tasks. With extensive experiments, we demonstrate the proposed ORDER alleviates forgetting on evolving task distributions and is more robust to OOD data than related strong baselines.
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