SHARCS: Shared Concept Space for Explainable Multimodal Learning (2307.00316v1)

Abstract: Multimodal learning is an essential paradigm for addressing complex real-world problems, where individual data modalities are typically insufficient to accurately solve a given modelling task. While various deep learning approaches have successfully addressed these challenges, their reasoning process is often opaque; limiting the capabilities for a principled explainable cross-modal analysis and any domain-expert intervention. In this paper, we introduce SHARCS (SHARed Concept Space) -- a novel concept-based approach for explainable multimodal learning. SHARCS learns and maps interpretable concepts from different heterogeneous modalities into a single unified concept-manifold, which leads to an intuitive projection of semantically similar cross-modal concepts. We demonstrate that such an approach can lead to inherently explainable task predictions while also improving downstream predictive performance. Moreover, we show that SHARCS can operate and significantly outperform other approaches in practically significant scenarios, such as retrieval of missing modalities and cross-modal explanations. Our approach is model-agnostic and easily applicable to different types (and number) of modalities, thus advancing the development of effective, interpretable, and trustworthy multimodal approaches.

• The paper introduces the SHARCS framework that maps human-interpretable concepts from various modalities into a unified space for explainable AI.
• It employs a tailored loss function to enforce semantic coherence across modalities, improving performance even with missing data.
• Experimental results on four multimodal tasks demonstrate superior accuracy and offer clear, interpretable decision insights.

Overview of SHARCS: A Model for Explainable Multimodal Learning

The paper introduces SHARCS (SHARed Concept Space), a novel approach aimed at advancing the field of explainable multimodal learning. Multimodal learning systems are essential in addressing complex real-world problems where single data modalities fall short. However, a significant challenge in this area is the opacity of deep learning models, which hinders interpretable cross-modal analysis. SHARCS proposes a solution to this challenge through the creation of a unified concept space that allows for the mapping of interpretable concepts across diverse modalities.

The proposed SHARCS framework stands out by shifting away from the traditional approach of combining unexplainable embeddings and instead focuses on combining human-interpretable concepts. These concepts are drawn from various modalities (such as image, text, and graph data) and projected into a shared space that facilitates intuitive, explainable predictions and enables improved downstream performance. The approach is model-agnostic, meaning it can be applied across different types and numbers of modalities without dependency on specific models.

A key aspect of the SHARCS framework is its learning mechanism, which focuses on constructing a semantically homogeneous shared space. This is achieved through a tailored loss function that minimizes the distance between semantically similar concepts from different modalities, thereby promoting cross-modal concept coherence. This regularization bolsters the model's ability to handle scenarios with missing modalities by utilizing the shared space to infer missing data, further demonstrating its practical applicability.

Experimental Validation

The authors validate SHARCS through a series of experiments focusing on four multimodal tasks incorporating tabular, image, graph, and text data. The results demonstrate that SHARCS consistently achieves superior performance compared to unimodal models and matches or outperforms existing multimodal approaches. Specifically, SHARCS exhibits robust accuracy across tasks, even in instances where data from certain modalities is missing. This capability is particularly important in practical applications where complete data may not always be available.

Moreover, the experimental analysis showcases the interpretability of SHARCS. By providing clarity into the model's decision-making process through a concept-based framework, it becomes possible to generate intuitively understandable explanations for task predictions. This interpretability is quantitatively measured using a completeness score, where SHARCS demonstrates a high degree of semantic clarity and compactness of learned concepts.

Implications and Future Developments

The implications of the SHARCS framework are significant for both practical applications and theoretical advancements in AI. Practically, it offers a way to create more interpretable AI systems that can operate effectively even in data-limited settings. Theoretically, SHARCS introduces a paradigm where shared, interpretable concept spaces enhance both model performance and understanding.

Looking ahead, future developments might explore further generalization of SHARCS to manage even more complex multimodal interactions and to refine its concept mapping to capture finer semantic distinctions. Additionally, extending SHARCS to real-world applications, such as biomedical diagnostics or autonomous transport, could significantly enhance the trustworthiness and transparency of AI systems in critical domains. Overall, SHARCS provides a valuable contribution to the quest for effective and explainable AI solutions in multimodal settings.