DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning
This work addresses the need for domain-agnostic self-supervised learning methods to reduce the effort required for new domains, though it is incremental as it primarily establishes a benchmark.
The authors tackled the problem of domain-specific self-supervised learning by introducing DABS, a domain-agnostic benchmark with seven diverse domains, and found that baseline algorithms e-Mix and ShED showed modest performance, indicating significant progress is needed for out-of-the-box solutions.
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.