  Multimodal meta-learning is a recent problem that extends conventional
few-shot meta-learning by generalizing its setup to diverse multimodal task
distributions. This setup makes a step towards mimicking how humans make use of
a diverse set of prior skills to learn new skills. Previous work has achieved
encouraging performance. In particular, in spite of the diversity of the
multimodal tasks, previous work claims that a single meta-learner trained on a
multimodal distribution can sometimes outperform multiple specialized
meta-learners trained on individual unimodal distributions. The improvement is
attributed to knowledge transfer between different modes of task distributions.
However, there is no deep investigation to verify and understand the knowledge
transfer between multimodal tasks. Our work makes two contributions to
multimodal meta-learning. First, we propose a method to quantify knowledge
transfer between tasks of different modes at a micro-level. Our quantitative,
task-level analysis is inspired by the recent transference idea from multi-task
learning. Second, inspired by hard parameter sharing in multi-task learning and
a new interpretation of related work, we propose a new multimodal meta-learner
that outperforms existing work by considerable margins. While the major focus
is on multimodal meta-learning, our work also attempts to shed light on task
interaction in conventional meta-learning. The code for this project is
available at https://miladabd.github.io/KML.
