  Recent advances in deep learning, in particular enabled by hardware advances
and big data, have provided impressive results across a wide range of
computational problems such as computer vision, natural language, or
reinforcement learning. Many of these improvements are however constrained to
problems with large-scale curated data-sets which require a lot of human labor
to gather. Additionally, these models tend to generalize poorly under both
slight distributional shifts and low-data regimes. In recent years, emerging
fields such as meta-learning or self-supervised learning have been closing the
gap between proof-of-concept results and real-life applications of machine
learning by extending deep-learning to the semi-supervised and few-shot
domains. We follow this line of work and explore spatio-temporal structure in a
recently introduced image-to-image translation problem in order to: i)
formulate a novel multi-task few-shot image generation benchmark and ii)
explore data augmentations in contrastive pre-training for image translation
downstream tasks. We present several baselines for the few-shot problem and
discuss trade-offs between different approaches. Our code is available at
https://github.com/irugina/meta-image-translation.
