  Data augmentation has become a crucial component to train state-of-the-art
visual representation models. However, handcrafting combinations of
transformations that lead to improved performances is a laborious task, which
can result in visually unrealistic samples. To overcome these limitations,
recent works have explored the use of generative models as learnable data
augmentation tools, showing promising results in narrow application domains,
e.g., few-shot learning and low-data medical imaging. In this paper, we
introduce a data augmentation module, called DA_IC-GAN, which leverages
instance-conditioned GAN generations and can be used off-the-shelf in
conjunction with most state-of-the-art training recipes. We showcase the
benefits of DA_IC-GAN by plugging it out-of-the-box into the supervised
training of ResNets and DeiT models on the ImageNet dataset, and achieving
accuracy boosts up to between 1%p and 2%p with the highest capacity models.
Moreover, the learnt representations are shown to be more robust than the
baselines when transferred to a handful of out-of-distribution datasets, and
exhibit increased invariance to variations of instance and viewpoints. We
additionally couple DA_IC-GAN with a self-supervised training recipe and show
that we can also achieve an improvement of 1%p in accuracy in some settings.
With this work, we strengthen the evidence on the potential of learnable data
augmentations to improve visual representation learning, paving the road
towards non-handcrafted augmentations in model training.
