Explaining in Diffusion: Explaining a Classifier Through Hierarchical Semantics with Text-to-Image Diffusion Models (2412.18604v1)

Abstract: Classifiers are important components in many computer vision tasks, serving as the foundational backbone of a wide variety of models employed across diverse applications. However, understanding the decision-making process of classifiers remains a significant challenge. We propose DiffEx, a novel method that leverages the capabilities of text-to-image diffusion models to explain classifier decisions. Unlike traditional GAN-based explainability models, which are limited to simple, single-concept analyses and typically require training a new model for each classifier, our approach can explain classifiers that focus on single concepts (such as faces or animals) as well as those that handle complex scenes involving multiple concepts. DiffEx employs vision-LLMs to create a hierarchical list of semantics, allowing users to identify not only the overarching semantic influences on classifiers (e.g., the 'beard' semantic in a facial classifier) but also their sub-types, such as 'goatee' or 'Balbo' beard. Our experiments demonstrate that DiffEx is able to cover a significantly broader spectrum of semantics compared to its GAN counterparts, providing a hierarchical tool that delivers a more detailed and fine-grained understanding of classifier decisions.

• The paper introduces DiffEx, a novel method using text-to-image diffusion models and hierarchical semantics to provide versatile explanations for classifier decisions across diverse domains.
• DiffEx employs a training-free approach leveraging a broad semantic corpus derived from VLMs and an efficient beam search method to identify and rank attributes impacting classifier outputs.
• Experiments show DiffEx effectively uncovers complex hierarchical explanations, outperforms existing methods in user studies, and has potential implications for trustworthy AI in high-stakes applications like healthcare.

Overview of "Explaining in Diffusion: Explaining a Classifier Through Hierarchical Semantics with Text-to-Image Diffusion Models"

The paper introduces DiffEx, an innovative method designed to elucidate the decision-making processes of classifiers by leveraging text-to-image diffusion models, specifically focusing on enhancing the explainability beyond the traditional generative adversarial network (GAN)-based models. Unlike GAN-based approaches, which typically require re-training for new classifiers and are largely limited to single-domain or single-concept explanations, DiffEx utilizes a combination of text-to-image diffusion models and vision-LLMs (VLMs) to offer a more versatile and comprehensive explanation mechanism.

Methodological Advancements

DiffEx is articulated around leveraging VLMs to establish a comprehensive semantic corpus for the diffusion model, which covers a broad array of domain-specific, hierarchical semantics. The authors employ a training-free approach that capitalizes on this semantic hierarchy, enabling the explanation of classifier decisions across multiple concepts and domains. This includes both straightforward single-object scenarios (e.g., facial features) and more complex scenes involving multiple objects, such as architectural features or complete landscapes.

The core algorithmic contribution is an efficient semantic search method based on beam search principles, which dynamically identifies and ranks semantic attributes that significantly impact classifier logits. This method efficiently navigates the semantic space by expanding only the most promising semantic paths at each decision point, maintaining computational feasibility while ensuring in-depth attribute evaluation.

Experimental Domains and Results

The paper presents a comprehensive suite of experiments across diverse domains including facial features, animal species classification, plant pathology, and medical imaging for retinal disease detection, amongst others. Notably, DiffEx showcases its ability to uncover intricate hierarchical explanations—such as the identification and impact analysis of nuanced features within broader categories like types of beards or plant leaf diseases—thereby offering insights into both coarse and fine-grained semantic impacts on classifier outputs.

For instance, DiffEx is adept at explaining classifier decisions within the fashion domain by assessing multiple interacting features of an outfit, and similarly in the food domain by recognizing features that influence food categorization. The experiments demonstrate that DiffEx not only explains the model decisions more comprehensively than its predecessors like StylEx but also exposes implicit biases, which could have practical consequences in model applications.

Quantitative Evaluation

The method’s efficacy was quantitatively validated through user studies comparing it to other explainability techniques such as Grad-CAM and StylEx. The results indicate DiffEx’s superiority in terms of both attribution clarity and the breadth of explanations it provides, underlining its potential to significantly enhance the interpretability of classifier decisions across a variety of complex domains.

Implications and Future Directions

Practically, DiffEx holds substantial promise for applications requiring transparent and interpretable AI systems, particularly in high-stakes fields such as healthcare and finance, where understanding the basis of automated decisions is crucial. Theoretically, it also opens new avenues for using hierarchical semantic extraction and interpretation in complex AI models, potentially influencing future research into more explainable and trustworthy AI models.

In speculative future developments, increasing the autonomy of semantic extraction processes via more sophisticated VLMs could further enhance DiffEx’s utility. Additionally, integrating this methodology with real-time classifier systems could provide on-the-fly interpretability in dynamic environments, thereby broadening its applicability and fostering trust in AI systems.