DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks (2407.14509v1)

Abstract: We propose a permutation-based explanation method for image classifiers. Current image-model explanations like activation maps are limited to instance-based explanations in the pixel space, making it difficult to understand global model behavior. In contrast, permutation based explanations for tabular data classifiers measure feature importance by comparing model performance on data before and after permuting a feature. We propose an explanation method for image-based models that permutes interpretable concepts across dataset images. Given a dataset of images labeled with specific concepts like captions, we permute a concept across examples in the text space and then generate images via a text-conditioned diffusion model. Feature importance is then reflected by the change in model performance relative to unpermuted data. When applied to a set of concepts, the method generates a ranking of feature importance. We show this approach recovers underlying model feature importance on synthetic and real-world image classification tasks.

• The paper introduces DEPICT, a novel method that transposes permutation importance to image classification using text-conditioned diffusion models.
• The paper validates DEPICT on synthetic and real-world datasets, outperforming methods like GradCAM and LIME in feature importance scoring.
• The paper demonstrates DEPICT’s potential for enhancing model transparency and fairness in critical applications, including medical imaging with MIMIC-CXR.

DEPICT: Utilizing Diffusion-Enabled Permutation for Image Classifier Explanation

In the advancement of explainable AI (XAI), techniques for interpreting the behavior of image classification models remain a significant challenge, particularly for complex models like deep neural networks (DNNs). Traditional methods such as activation maps have shown limitations in conveying global model behavior or feature importance effectively. Addressing these challenges, the paper "DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks", authored by Sarah Jabbour, Gregory Kondas, Ella Kazerooni, Michael Sjoding, David Fouhey, and Jenna Wiens, introduces a novel permutation-based explanation method designed to elucidate the feature importance in image classification models.

Methodology

The core novelty of DEPICT lies in its ability to transpose the concept of permutation importance from tabular data to image data through the use of text-conditioned diffusion models. The technique involves several key steps:

1. Concept Permutation in Text Space: The method starts by permuting concepts in the text space, which is much more intuitive and feasible compared to the pixel space. Specifically, captions describing the presence of objects within images are shuffled.
2. Image Generation via Diffusion Models: Post permutation, a text-conditioned diffusion model generates new images based on the permuted captions. These diffusion models allow the smooth translation of permuted text (i.e., captions) back to the image space.
3. Measuring Performance Drop: The generated images are then fed into the image classification model to measure the performance drop compared to the baseline (unpermuted text), thereby indicating the importance of the permuted concept.

Validation and Results

The efficacy of DEPICT was validated through both synthetic datasets and real-world datasets like COCO and MIMIC-CXR. The synthetic dataset experiments demonstrated the method's validity by correlating DEPICT's output with standardized regression weights, showing high alignment.

In the case of COCO, the models designed to rely on primary concepts such as 'person' or 'couch' showed that DEPICT correctly identified the significant features compared to baseline methods like GradCAM and LIME, with higher correlation with the oracle. For models trained on mixed features, DEPICT continued to outperform traditional methods, showcasing its robustness.

The application in the MIMIC-CXR dataset highlighted how DEPICT can be used in practical settings. Particularly, it addressed a salient issue in medical AI by verifying that models do not unduly rely on irrelevant demographic features (age, BMI, sex) unless correlated with medical conditions, thus providing a crucial tool for validating model fairness and robustness.

Implications and Future Work

The implications of DEPICT's findings point towards substantial advancements in the interpretability of image classifiers through global, concept-level explanations rather than instance-based methods. The methodology ensures generalization across different tasks, making it a versatile tool for diverse applications ranging from everyday object detection to critical domains like healthcare diagnosis.

Future developments could enhance the efficacy of text-conditioned diffusion models. As these generative models improve, DEPICT's accuracy and applicability are expected to broaden, potentially enabling real-time and more nuanced interpretability tools.

Conclusion

DEPICT represents a significant stride in the quest for model transparency by addressing the hitherto difficult challenge of explaining image models at a conceptual level. By leveraging diffusion models to bridge text and image spaces, DEPICT proves not only the feasibility but also the superiority of permutation-based feature importance in complex image data contexts. This methodological innovation promises to equip stakeholders with better tools for model validation, ultimately pushing the boundaries of safe and interpretable AI deployment.