Learning with auxiliary tasks can improve the ability of a primary task to generalise.
However, this comes at the cost of manually labelling auxiliary data. We propose a
new method which automatically learns appropriate labels for an auxiliary task,
such that any supervised learning task can be improved without requiring access to
any further data. The approach is to train two neural networks: a label-generation
network to predict the auxiliary labels, and a multi-task network to train the
primary task alongside the auxiliary task. The loss for the label-generation network
incorporates the loss of the multi-task network, and so this interaction between the
two networks can be seen as a form of meta learning with a double gradient. We
show that our proposed method, Meta AuXiliary Learning (MAXL), outperforms
single-task learning on 7 image datasets, without requiring any additional data.
We also show that MAXL outperforms several other baselines for generating
auxiliary labels, and is even competitive when compared with human-defined
auxiliary labels. The self-supervised nature of our method leads to a promising
new direction towards automated generalisation. Source code can be found at
https://github.com/lorenmt/maxl.