It is expensive to collect training data for every possible domain that a
vision model may encounter when deployed. We instead consider how simply
verbalizing the training domain (e.g. "photos of birds") as well as domains we
want to extend to but do not have data for (e.g. "paintings of birds") can
improve robustness. Using a multimodal model with a joint image and language
embedding space, our method LADS learns a transformation of the image
embeddings from the training domain to each unseen test domain, while
preserving task relevant information. Without using any images from the unseen
test domain, we show that over the extended domain containing both training and
unseen test domains, LADS outperforms standard fine-tuning and ensemble
approaches over a suite of four benchmarks targeting domain adaptation and
dataset bias