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

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 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.
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 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.
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
http://arxiv.org/abs/2107.07455v3