A Channel Coding Benchmark for Meta-Learning

Rui Li, Ondrej Bohdal, Rajesh K Mishra, Hyeji Kim, Da Li, Nicholas Donald Lane, Timothy Hospedales

Keywords: channel coding, meta-learning, benchmark, communication theory, wireless communications, information theory, software defined radio, testbeds

Abstract: Meta-learning provides a popular and effective family of methods for data-efficient learning of new tasks. However, several important issues in meta-learning have proven hard to study thus far. For example, performance degrades in real-world settings where meta-learners must learn from a wide and potentially multi-modal distribution of training tasks; and when distribution shift exists between meta-train and meta-test task distributions. These issues are typically hard to study since the shape of task distributions, and shift between them are not straightforward to measure or control in standard benchmarks. We propose the channel coding problem as a benchmark for meta-learning. Channel coding is an important practical application where task distributions naturally arise, and fast adaptation to new tasks is practically valuable. We use this benchmark to study several aspects of meta-learning, including the impact of task distribution breadth and shift on meta-learner performance, which can be controlled in the coding problem. Going forward, this benchmark provides a tool for the community to study the capabilities and limitations of meta-learning, and to drive research on practically robust and effective meta-learners.

URL: https://github.com/ruihuili/MetaCC.git

TL;DR: We propose channel coding as a novel benchmark to study several aspects of meta-learning, including the impact of task distribution breadth and shift on meta-learner performance, which can be controlled in the coding problem.

Contribution Process Agreement: Yes

Dataset Url: https://github.com/ruihuili/MetaCC/tree/master/dataset

License: Apache License 2.0

Author Statement: Yes