  Recent advances in learning aligned multimodal representations have been
primarily driven by training large neural networks on massive, noisy
paired-modality datasets. In this work, we ask whether it is possible to
achieve similar results with substantially less training time and data. We
achieve this by taking advantage of existing pretrained unimodal encoders and
careful curation of alignment data relevant to the downstream task of interest.
We study a natural approach to aligning existing encoders via small auxiliary
functions, and we find that this method is competitive with (or outperforms)
state of the art in many settings while being less prone to overfitting, less
costly to train, and more robust to distribution shift. With a properly chosen
alignment distribution, our method surpasses prior state of the art for
ImageNet zero-shot classification on public data while using two orders of
magnitude less time and data and training 77% fewer parameters.
