  Self-supervised learning algorithms, including BERT and SimCLR, have enabled
significant strides in fields like natural language processing, computer
vision, and speech processing. However, these algorithms are domain-specific,
meaning that new self-supervised learning algorithms must be developed for each
new setting, including myriad healthcare, scientific, and multimodal domains.
To catalyze progress toward domain-agnostic methods, we introduce DABS: a
Domain-Agnostic Benchmark for Self-supervised learning. To perform well on
DABS, an algorithm is evaluated on seven diverse domains: natural images,
multichannel sensor data, English text, speech recordings, multilingual text,
chest x-rays, and images with text descriptions. Each domain contains an
unlabeled dataset for pretraining; the model is then is scored based on its
downstream performance on a set of labeled tasks in the domain. We also present
e-Mix and ShED: two baseline domain-agnostic algorithms; their relatively
modest performance demonstrates that significant progress is needed before
self-supervised learning is an out-of-the-box solution for arbitrary domains.
Code for benchmark datasets and baseline algorithms is available at
https://github.com/alextamkin/dabs.
