  Data augmentation methods have played an important role in the recent advance
of deep learning models, and have become an indispensable component of
state-of-the-art models in semi-supervised, self-supervised, and supervised
training for vision. Despite incurring no additional latency at test time, data
augmentation often requires more epochs of training to be effective. For
example, even the simple flips-and-crops augmentation requires training for
more than 5 epochs to improve performance, whereas RandAugment requires more
than 90 epochs. We propose a general framework called Tied-Augment, which
improves the efficacy of data augmentation in a wide range of applications by
adding a simple term to the loss that can control the similarity of
representations under distortions. Tied-Augment can improve state-of-the-art
methods from data augmentation (e.g. RandAugment, mixup), optimization (e.g.
SAM), and semi-supervised learning (e.g. FixMatch). For example,
Tied-RandAugment can outperform RandAugment by 2.0% on ImageNet. Notably, using
Tied-Augment, data augmentation can be made to improve generalization even when
training for a few epochs and when fine-tuning. We open source our code at
https://github.com/ekurtulus/tied-augment/tree/main.
