Self-supervised learning is a powerful paradigm for representation learning
on unlabelled images. A wealth of effective new methods based on instance
matching rely on data-augmentation to drive learning, and these have reached a
rough agreement on an augmentation scheme that optimises popular recognition
benchmarks. However, there is strong reason to suspect that different tasks in
computer vision require features to encode different (in)variances, and
therefore likely require different augmentation strategies. In this paper, we
measure the invariances learned by contrastive methods and confirm that they do
learn invariance to the augmentations used and further show that this
invariance largely transfers to related real-world changes in pose and
lighting. We show that learned invariances strongly affect downstream task
performance and confirm that different downstream tasks benefit from polar
opposite (in)variances, leading to performance loss when the standard
augmentation strategy is used. Finally, we demonstrate that a simple fusion of
representations with complementary invariances ensures wide transferability to
all the diverse downstream tasks considered.

本文研究了自监督学习在图像中表征学习的应用，通过对比实例匹配方法，我们发现不同的视觉任务需要不同的数据扩充策略，并且使用具有互补不变性的表征方法可以提高各种下游任务的表现。