Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time (2211.14238v2)

Abstract: Distribution shift occurs when the test distribution differs from the training distribution, and it can considerably degrade performance of machine learning models deployed in the real world. Temporal shifts -- distribution shifts arising from the passage of time -- often occur gradually and have the additional structure of timestamp metadata. By leveraging timestamp metadata, models can potentially learn from trends in past distribution shifts and extrapolate into the future. While recent works have studied distribution shifts, temporal shifts remain underexplored. To address this gap, we curate Wild-Time, a benchmark of 5 datasets that reflect temporal distribution shifts arising in a variety of real-world applications, including patient prognosis and news classification. On these datasets, we systematically benchmark 13 prior approaches, including methods in domain generalization, continual learning, self-supervised learning, and ensemble learning. We use two evaluation strategies: evaluation with a fixed time split (Eval-Fix) and evaluation with a data stream (Eval-Stream). Eval-Fix, our primary evaluation strategy, aims to provide a simple evaluation protocol, while Eval-Stream is more realistic for certain real-world applications. Under both evaluation strategies, we observe an average performance drop of 20% from in-distribution to out-of-distribution data. Existing methods are unable to close this gap. Code is available at https://wild-time.github.io/.

• The paper introduces Wild-Time, a benchmark that assesses model robustness against temporal distribution shifts.
• It evaluates five real-world datasets using fixed and stream strategies, revealing an average performance drop of 20% on out-of-distribution data.
• The findings underscore the need for innovative methods to enhance ML model reliability in dynamic, time-evolving environments.

Overview of "Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time"

The paper "Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time" introduces a novel benchmark designed to evaluate the robustness of machine learning models against temporal distribution shifts. Distribution shifts pose significant challenges when models are deployed in real-world scenarios, where the test data often diverges from the data the model was trained on. Temporal shifts, which naturally occur over time, are particularly troublesome and require comprehensive exploration and understanding.

The benchmark, termed "Wild-Time", encompasses five datasets across various domains such as portraits classification, healthcare predictions, news classification, and academic paper categorization. These datasets reflect real-world temporal shifts and incorporate timestamp metadata, allowing for an evaluation of models' ability to generalize across time.

Key Findings

The authors evaluate several existing approaches to handle distribution shifts, including methods from domain generalization, continual learning, self-supervised learning, and ensemble learning. The evaluation is conducted using two strategies: Eval-Fix, which divides the dataset into a fixed train-test split based on timestamps, and Eval-Stream, which simulates a continuous data stream.

Notably, the paper reports an average performance degradation of 20% when moving from in-distribution data to out-of-distribution data. Despite the variety of methods tested, none showed significant improvements in bridging this performance gap, indicating substantial room for advancements in this area.

Numerical Results and Claims

The numerical evidence presented is stark, with substantial drops in model accuracy and AUC scores across all datasets. For instance, in the Yearbook dataset, the average accuracy dropped from 97.99% in in-distribution scenarios to 79.50% in out-of-distribution evaluations. Similarly, the MIMIC-Mortality dataset exhibited a decline in AUC from 90.89% to 72.89%. These figures underscore the challenge of temporal shifts and the current inadequacy of existing techniques in addressing these shifts.

Implications and Future Directions

In practical terms, the implications of this work are profound for any AI applications deployed in dynamic environments. The inability to maintain performance over time suggests a need for developing new strategies that can adapt to temporal changes more effectively. Theoretically, this benchmark could drive research toward developing frameworks that can better leverage temporal data, adapting models continuously as new data streams in.

The authors encourage further exploration in temporally robust model design and have provided the Wild-Time datasets as an accessible resource for future research endeavors. As advances are made, these efforts could lead to more reliable, dependable AI systems across industries, from healthcare to finance, where temporal shifts are inevitable.

Conclusion

The "Wild-Time" paper offers a foundational benchmark for understanding and evaluating temporal distribution shifts in machine learning models. By highlighting the deficiencies of current approaches, it sets the stage for future innovations in AI model design and deployment, ensuring models maintain robustness and reliability amid the passage of time.