Self-Supervised Generalisation with Meta Auxiliary Learning

We showthatourproposedmethod,MetaAuXiliaryLearning(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.