Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale TasksDownload PDF

Aug 18, 2021 (edited Nov 09, 2021)NeurIPS 2021 Datasets and Benchmarks Track (Round 2)Readers: Everyone
  • Keywords: Distributional shift, uncertainty, robustness, tabular data, machine translation, vehicle motion prediction, dataset and baselines
  • TL;DR: We propose the Shifts Dataset for investigating distributional shift and uncertainty estimation estimation on a range of industrial scale tasks across multiple data modalities.
  • Abstract: There has been significant research done on developing methods for improving robustness to distributional shift and uncertainty estimation. In contrast, only limited work has examined developing standard datasets and benchmarks for assessing these approaches. Additionally, most work on uncertainty estimation and robustness has developed new techniques based on small-scale regression or image classification tasks. However, many tasks of practical interest have different modalities, such as tabular data, audio, text, or sensor data, which offer significant challenges involving regression and discrete or continuous structured prediction. Thus, given the current state of the field, a standardized large-scale dataset of tasks across a range of modalities affected by distributional shifts is necessary. This will enable researchers to meaningfully evaluate the plethora of recently developed uncertainty quantification methods, as well as assessment criteria and state-of-the-art baselines. In this work, we propose the \emph{Shifts Dataset} for evaluation of uncertainty estimates and robustness to distributional shift. The dataset, which has been collected from industrial sources and services, is composed of three tasks, with each corresponding to a particular data modality: tabular weather prediction, machine translation, and self-driving car (SDC) vehicle motion prediction. All of these data modalities and tasks are affected by real, `in-the-wild' distributional shifts and pose interesting challenges with respect to uncertainty estimation. In this work we provide a description of the dataset and baseline results for all tasks.
  • Supplementary Material: zip
  • URL: https://github.com/yandex-research/shifts - Links to the dataset and metadata will be provided here. However, currently only the canonically partitioned training and dev data is provided, as the data is used in the currently on-going Shifts Challenge. Full dataset and meta will be released 1.11.2021 after the Challenge concludes.
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