A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others (2212.04825v2)

Abstract: Machine learning models have been found to learn shortcuts -- unintended decision rules that are unable to generalize -- undermining models' reliability. Previous works address this problem under the tenuous assumption that only a single shortcut exists in the training data. Real-world images are rife with multiple visual cues from background to texture. Key to advancing the reliability of vision systems is understanding whether existing methods can overcome multiple shortcuts or struggle in a Whac-A-Mole game, i.e., where mitigating one shortcut amplifies reliance on others. To address this shortcoming, we propose two benchmarks: 1) UrbanCars, a dataset with precisely controlled spurious cues, and 2) ImageNet-W, an evaluation set based on ImageNet for watermark, a shortcut we discovered affects nearly every modern vision model. Along with texture and background, ImageNet-W allows us to study multiple shortcuts emerging from training on natural images. We find computer vision models, including large foundation models -- regardless of training set, architecture, and supervision -- struggle when multiple shortcuts are present. Even methods explicitly designed to combat shortcuts struggle in a Whac-A-Mole dilemma. To tackle this challenge, we propose Last Layer Ensemble, a simple-yet-effective method to mitigate multiple shortcuts without Whac-A-Mole behavior. Our results surface multi-shortcut mitigation as an overlooked challenge critical to advancing the reliability of vision systems. The datasets and code are released: https://github.com/facebookresearch/Whac-A-Mole.

Analyzing the Challenge of Multi-Shortcut Mitigation in Computer Vision Models

The paper "Shortcuts Come in Multiples Where Mitigating One Amplifies Others" addresses a critical and underexplored aspect of the computer vision field—namely, the multi-shortcut problem. The focus is on how mitigating one shortcut within machine learning models can inadvertently amplify reliance on others, a phenomenon the authors metaphorically describe as a "Whac-A-Mole" scenario.

Key Contributions and Findings

The authors contribute to the field through several significant and structured advancements:

1. Introduction of Datasets: Two new datasets, UrbanCars and ImageNet-W, are introduced to better evaluate the existence of multiple shortcuts in computer vision models. UrbanCars is a synthetic dataset designed with controlled spurious correlations around car images, while ImageNet-W is an out-of-distribution (OOD) variant of ImageNet enhanced by the discovery of a "watermark" shortcut in the classic ImageNet dataset.
2. Comprehensive Benchmarking: The paper rigorously benchmarks a range of contemporary vision models including ResNet-50, foundational models like CLIP, and those employing various regularization techniques. Across these models, the authors identify prevalent issues in overcoming multiple shortcuts.
3. Proposal of Last Layer Ensemble (LLE): To address the Whac-A-Mole problem, the authors propose the Last Layer Ensemble (LLE) method. This is an ensemble method where each classifier in the ensemble is trained to address different types of shortcuts independently. The ensemble's predictions are dynamically aggregated based on the predicted distributional shift type associated with a given input, alleviating the complexity of shortcut interference.

Empirical Results

Key numerical results illustrate the pervasive and challenging nature of multi-shortcut dependencies:

• On UrbanCars, standard approaches like ERM showed substantial drops in accuracy when spurious shortcuts are disrupted: a drop of 15.3% on backgrounds and 11.2% on co-occurring objects, indicating these models' heavy reliance on shortcuts.
• ImageNet-W, which introduces the watermark shortcut, showed that models like ResNet-50 suffer a significant accuracy drop of up to 26.7%, reinforcing the concept that current models leverage such unintended correlations as shortcuts for classification tasks.
• Despite extensive training on additional data, many modern models, including those leveraging large foundation datasets, display Whac-A-Mole dilemmas, where resolution improves one aspect while simultaneously degrading another.
• With LLE, the paper demonstrates improved effectiveness in mitigating multiple shortcuts simultaneously without substantial degradation on others, outperforming other methods in key metrics across both urban and real-world benchmarks.

Implications and Future Work

The paper's findings suggest a need for redesigning models and training paradigms to account for the multi-faceted nature of shortcuts in real-world scenarios. The existence of multiple interacting shortcuts challenges simplistic models of learning robustness and calls into question the one-dimensional focus of many accuracy enhancement strategies.

Looking forward, the research implies a growing need for frameworks that can dynamically adapt to complex input distributions, perhaps integrating meta-learning aspects or more cognitively inspired models that factor in environmental complexity. Furthermore, the tension between efficiency (e.g., using last layer re-training) and effective shortcut mitigation suggests potential areas for algorithmic innovation.

In conclusion, this paper shines light on a crucial dimension of machine learning model design that necessitates ongoing inquiry and reassessment of current standard practices. It calls for broader exploration into how inherent model biases and historical training inefficiencies may perpetuate unforeseen vulnerabilities in automated decision systems.