  Self-supervised representation learning is an emerging research topic for its
powerful capacity in learning with unlabeled data. As a mainstream
self-supervised learning method, augmentation-based contrastive learning has
achieved great success in various computer vision tasks that lack manual
annotations. Despite current progress, the existing methods are often limited
by extra cost on memory or storage, and their performance still has large room
for improvement. Here we present a self-supervised representation learning
method, namely AAG, which is featured by an auxiliary augmentation strategy and
GNT-Xent loss. The auxiliary augmentation is able to promote the performance of
contrastive learning by increasing the diversity of images. The proposed
GNT-Xent loss enables a steady and fast training process and yields competitive
accuracy. Experiment results demonstrate the superiority of AAG to previous
state-of-the-art methods on CIFAR10, CIFAR100, and SVHN. Especially, AAG
achieves 94.5% top-1 accuracy on CIFAR10 with batch size 64, which is 0.5%
higher than the best result of SimCLR with batch size 1024.
