Meta-learning approaches have been proposed to tackle the few-shot learning
problem.Typically, a meta-learner is trained on a variety of tasks in the hopes
of being generalizable to new tasks. However, the generalizability on new tasks
of a meta-learner could be fragile when it is over-trained on existing tasks
during meta-training phase. In other words, the initial model of a meta-learner
could be too biased towards existing tasks to adapt to new tasks, especially
when only very few examples are available to update the model. To avoid a
biased meta-learner and improve its generalizability, we propose a novel
paradigm of Task-Agnostic Meta-Learning (TAML) algorithms. Specifically, we
present an entropy-based approach that meta-learns an unbiased initial model
with the largest uncertainty over the output labels by preventing it from
over-performing in classification tasks. Alternatively, a more general
inequality-minimization TAML is presented for more ubiquitous scenarios by
directly minimizing the inequality of initial losses beyond the classification
tasks wherever a suitable loss can be defined.Experiments on benchmarked
datasets demonstrate that the proposed approaches outperform compared
meta-learning algorithms in both few-shot classification and reinforcement
learning tasks.

本文提出了一种基于熵和不等式优化的任务不可知元学习算法，可以在避免过度适应已有任务和提高元学习器泛化性能方面有效地解决少样本学习问题，并在少量分类和强化学习任务中表现优异。