  Optimization-based meta-learning typically assumes tasks are sampled from a
single distribution - an assumption oversimplifies and limits the diversity of
tasks that meta-learning can model. Handling tasks from multiple different
distributions is challenging for meta-learning due to a so-called task
ambiguity issue. This paper proposes a novel method, ST-MAML, that empowers
model-agnostic meta-learning (MAML) to learn from multiple task distributions.
ST-MAML encodes tasks using a stochastic neural network module, that summarizes
every task with a stochastic representation. The proposed Stochastic Task (ST)
strategy allows a meta-model to get tailored for the current task and enables
us to learn a distribution of solutions for an ambiguous task. ST-MAML also
propagates the task representation to revise the encoding of input variables.
Empirically, we demonstrate that ST-MAML matches or outperforms the
state-of-the-art on two few-shot image classification tasks, one curve
regression benchmark, one image completion problem, and a real-world
temperature prediction application. To the best of authors' knowledge, this is
the first time optimization-based meta-learning method being applied on a
large-scale real-world task.
