SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning
This addresses the challenge of guiding unsupervised training in domains like medical imaging where labels are unavailable or privacy-sensitive, though it is incremental as it builds on existing self-supervised methods.
The paper tackled the problem of selecting data augmentation policies for self-supervised learning without using labeled data, by showing that a self-supervised image rotation task correlates highly with supervised evaluations (rank correlation > 0.94) and developing an algorithm (SelfAugment) that performs comparably to policies selected with supervised evaluations.
A common practice in unsupervised representation learning is to use labeled data to evaluate the quality of the learned representations. This supervised evaluation is then used to guide critical aspects of the training process such as selecting the data augmentation policy. However, guiding an unsupervised training process through supervised evaluations is not possible for real-world data that does not actually contain labels (which may be the case, for example, in privacy sensitive fields such as medical imaging). Therefore, in this work we show that evaluating the learned representations with a self-supervised image rotation task is highly correlated with a standard set of supervised evaluations (rank correlation $> 0.94$). We establish this correlation across hundreds of augmentation policies, training settings, and network architectures and provide an algorithm (SelfAugment) to automatically and efficiently select augmentation policies without using supervised evaluations. Despite not using any labeled data, the learned augmentation policies perform comparably with augmentation policies that were determined using exhaustive supervised evaluations.