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Tackling Shortcut Learning in Deep Neural Networks: An Iterative Approach with Interpretable Models (2302.10289v9)

Published 20 Feb 2023 in cs.LG and cs.CV

Abstract: We use concept-based interpretable models to mitigate shortcut learning. Existing methods lack interpretability. Beginning with a Blackbox, we iteratively carve out a mixture of interpretable experts (MoIE) and a residual network. Each expert explains a subset of data using First Order Logic (FOL). While explaining a sample, the FOL from biased BB-derived MoIE detects the shortcut effectively. Finetuning the BB with Metadata Normalization (MDN) eliminates the shortcut. The FOLs from the finetuned-BB-derived MoIE verify the elimination of the shortcut. Our experiments show that MoIE does not hurt the accuracy of the original BB and eliminates shortcuts effectively.

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Citations (2)
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Summary

  • The paper proposes an iterative method that uses a Mixture of Interpretable Experts (MoIE) to detect and eliminate shortcut learning in blackbox deep neural networks.
  • Numerical results demonstrate the method's effectiveness in improving worst-group accuracy on datasets like Waterbirds, indicating enhanced robustness and generalization.
  • The findings have practical implications for deploying robust models in critical domains and theoretically explore integrating blackbox and interpretable AI systems.

Tackling Shortcut Learning in Deep Neural Networks: An Iterative Approach with Interpretable Models

The paper "Tackling Shortcut Learning in Deep Neural Networks: An Iterative Approach with Interpretable Models" addresses the issue of shortcut learning in deep neural networks. Shortcut learning, which involves the model relying on spurious correlations rather than meaningful features, poses a significant challenge to the generalizability of deep neural networks. This problem is especially concerning in high-stakes applications, such as medical diagnosis. The authors propose a novel method aimed at mitigating shortcut learning by iteratively distilling a mixture of interpretable models, referred to as the Mixture of Interpretable Experts (MoIE), from a given blackbox (BB) model.

Methodological Overview

The proposed method comprises several key steps:

  1. Detection: The initial BB, suspected of shortcut learning, is distilled into several interpretable experts and a residual network. Each expert is specialized in explaining a subset of the data using First Order Logic (FOL). This step aims to identify the spurious correlations being used by the model.
  2. Elimination: Once the shortcuts are identified, the BB is fine-tuned using Metadata Normalization (MDN) to eliminate the effect of these extraneous correlations. The MDN layers normalize the influence of certain metadata, thereby removing shortcut dependencies.
  3. Verification: After fine-tuning, the MoIE is updated with the newly fine-tuned BB to verify whether the spuriously learned shortcuts have been effectively eliminated. The updated MoIE should no longer detect these spurious correlations in its explanations.

This iterative approach ensures that the interpretable models crafted from the BB can effectively identify and remove shortcuts without compromising the BB's original predictive performance.

Numerical Results and Insights

The authors conducted extensive experiments across several datasets and neural network architectures, including ResNet, DenseNet, and Vision Transformers (VITs), to assess the efficacy of their method. Notably, the method achieved substantial success in maintaining the performance of the original BB models while effectively mitigating shortcut learning. For instance, on the Waterbirds dataset, the method significantly improved the worst-group accuracy to 93.7%, compared to the lower performances demonstrated by traditional approaches like Invariant Risk Minimization (IRM) and Group Distributionally Robust Optimization (GroupDRO).

Additionally, the MoIE was shown to achieve robust performance across various datasets, often outperforming existing concept-based models, both interpretable-by-design and post hoc, in generating accurate and diverse explanations.

Implications and Future Directions

Practical implications of this research are significant, particularly in domains where model robustness and interpretability are critical. By ensuring that models do not rely on spurious correlations, practitioners can deploy machine learning systems with higher confidence in their generalizability. The MoIE approach could be particularly beneficial in medical imaging tasks, enhancing patient safety by providing reliable and interpretable predictions.

Theoretically, this work bridges the dichotomy between blackbox models and interpretable systems, suggesting a path where post hoc interpretability can be converted into inherently interpretable models. Moreover, using interpretable experts to dissect BBs might lay the groundwork for future AI systems capable of human-like reasoning processes.

Future research directions could involve extending this framework to tackle more complex forms of shortcut learning, integrating other forms of interpretability, or exploring its implications in other machine learning contexts, such as reinforcement learning or natural language processing. Additionally, further exploration into automatizing the process of detecting and defining what constitutes a "shortcut" in various domains could enhance the method's applicability.

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