Self-Supervised Generalisation with Meta Auxiliary Learning

TL;DR

This paper introduces MAXL, a self-supervised meta auxiliary learning method that automatically generates auxiliary labels to improve generalization across multiple image datasets without extra data.

Contribution

The paper presents MAXL, a novel self-supervised meta learning approach that automatically learns auxiliary labels, enhancing primary task performance without additional data or manual labeling.

Findings

MAXL outperforms single-task learning on 7 image datasets.

MAXL surpasses other auxiliary label generation baselines.

MAXL is competitive with human-defined auxiliary labels.

Abstract

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.