DABS: a Domain-Agnostic Benchmark for Self-Supervised LearningDownload PDF

Jun 08, 2021 (edited Jan 10, 2022)NeurIPS 2021 Datasets and Benchmarks Track (Round 1)Readers: Everyone
  • Keywords: domain agnostic, self-supervised learning, benchmark, representation learning, unsupervised learning, transfer learning
  • TL;DR: We present a benchmark for the task of domain-agnostic self-supervised learning, along with two baseline algorithms based on transformers
  • Abstract: 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.
  • URL: https://dabs.stanford.edu/
  • Contribution Process Agreement: Yes
  • Dataset Url: dabs.stanford.edu
  • License: MIT
  • Author Statement: Yes
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