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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 mimics 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 analyze and quantify knowledge transfer across 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.
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