  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.
