BREEDS: Benchmarks for Subpopulation Shift (2008.04859v1)

Abstract: We develop a methodology for assessing the robustness of models to subpopulation shift---specifically, their ability to generalize to novel data subpopulations that were not observed during training. Our approach leverages the class structure underlying existing datasets to control the data subpopulations that comprise the training and test distributions. This enables us to synthesize realistic distribution shifts whose sources can be precisely controlled and characterized, within existing large-scale datasets. Applying this methodology to the ImageNet dataset, we create a suite of subpopulation shift benchmarks of varying granularity. We then validate that the corresponding shifts are tractable by obtaining human baselines for them. Finally, we utilize these benchmarks to measure the sensitivity of standard model architectures as well as the effectiveness of off-the-shelf train-time robustness interventions. Code and data available at https://github.com/MadryLab/BREEDS-Benchmarks .

• The paper introduces a novel framework that repurposes existing datasets to simulate controlled subpopulation shifts for evaluating model generalization.
• It demonstrates that standard models experience over 30% performance declines from observed to unseen subpopulations, exposing their robustness vulnerabilities.
• The findings reveal that while interventions like adversarial training improve robustness, a significant gap remains compared to human-level performance.

Evaluation of Robustness to Subpopulation Shift Using Breeds Benchmarks

The paper "Breeds: Benchmarks for Subpopulation Shift" addresses a pertinent concern in machine learning regarding model robustness to unseen data distributions. The authors develop a novel methodology for evaluating the generalization capabilities of models to subpopulations not observed during training. This presents a significant advancement in the domain of machine learning, particularly for assessing the versatility and robustness of models when subjected to varying real-world conditions.

Methodological Overview

The authors introduce a framework that repurposes existing datasets, such as ImageNet, to create new benchmarks focusing on subpopulation shifts. These shifts are crafted by leveraging the intrinsic class structure within datasets, allowing for the controlled simulation of distribution shifts without needing additional data collection. This strategy efficiently utilizes the hierarchical structure of datasets while minimizing potential biases introduced by synthetic data manipulations.

The essence of the Breeds methodology lies in its ability to delineate superclasses, or clusters of semantically similar classes, from existing datasets. By modifying hierarchical structures like those in ImageNet (based on WordNet), the authors create a suite of classification tasks where observed training data and unobserved test data subpopulations are disjoint yet semantically connected. This methodology introduces benchmarks of varying difficulty, allowing for a comprehensive assessment of model robustness across different levels of data granularity.

Insights into Model Robustness

Using their Breeds benchmarks, the authors examine the robustness of standard model architectures, as well as the efficacy of various train-time robustness interventions. A significant finding is that traditional models experience substantial performance declines, approximately over 30% on average when transitioning from familiar to unseen subpopulation distributions. Interestingly, models with higher accuracy on initial data tend to showcase better robustness against subpopulation shifts, suggesting a positive correlation between general accuracy and robustness.

Furthermore, interventions like adversarial training, stylized training, and random noise addition were analyzed for their impact on model robustness. Although these interventions contribute marginal improvements in relative accuracy when facing shift challenges, they fall short of fully mitigating distribution sensitivity. However, some interventions, particularly adversarial training, demonstrated enhanced improvements when models were adapted to new data distributions, accentuating the potential of feature priors in enhancing model generalization.

Human vs. Model Baselines

To ground their benchmarks, the authors conducted human studies which confirmed that while subpopulation shifts minimally impact human annotators, models are considerably less robust. These studies serve as a comparative baseline, highlighting the gaps between model performance and human-level understanding, especially in terms of generalizing to unseen conditions.

Future Developments and Implications

This research underscores the necessity for model testbeds that accurately reflect real-world variability. The Breeds benchmarks provide an essential tool for probing the limits of current models and iterating towards more robust solutions. Moving forward, the Breeds methodology can be extended to a variety of domains beyond image classification, potentially aiding fields such as natural language processing, where understanding and adapting to diverse contextual shifts is crucial.

In conclusion, while existing models exhibit significant shortcomings in adapting to subpopulation shifts, the Breeds benchmarks establish a valuable framework for robust model evaluation. Future efforts should focus on developing more nuanced interventions and architectures that can inherently capture and adapt to the inherent variability present in real-world data distributions.