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

• URL: http://arxiv.org/abs/2107.07455v1
• Date: Thu, 15 Jul 2021 16:59:34 GMT
• Title: Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
• Authors: Andrey Malinin and Neil Band and German Chesnokov and Yarin Gal and Mark J. F. Gales and Alexey Noskov and Andrey Ploskonosov and Liudmila Prokhorenkova and Ivan Provilkov and Vatsal Raina and Vyas Raina and Mariya Shmatova and Panos Tigas and Boris Yangel
• Abstract summary: 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. We propose the emphShifts dataset for evaluation of uncertainty estimates and robustness to distributional shift.
• Score: 44.61070965407907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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