Towards an Explainable Comparison and Alignment of Feature Embeddings (2506.06231v1)

Abstract: While several feature embedding models have been developed in the literature, comparisons of these embeddings have largely focused on their numerical performance in classification-related downstream applications. However, an interpretable comparison of different embeddings requires identifying and analyzing mismatches between sample groups clustered within the embedding spaces. In this work, we propose the \emph{Spectral Pairwise Embedding Comparison (SPEC)} framework to compare embeddings and identify their differences in clustering a reference dataset. Our approach examines the kernel matrices derived from two embeddings and leverages the eigendecomposition of the difference kernel matrix to detect sample clusters that are captured differently by the two embeddings. We present a scalable implementation of this kernel-based approach, with computational complexity that grows linearly with the sample size. Furthermore, we introduce an optimization problem using this framework to align two embeddings, ensuring that clusters identified in one embedding are also captured in the other model. We provide numerical results demonstrating the SPEC's application to compare and align embeddings on large-scale datasets such as ImageNet and MS-COCO. The code is available at https://github.com/mjalali/embedding-comparison.

• The paper introduces the SPEC framework, which uses eigendecomposition of difference kernel matrices to compare feature embeddings effectively.
• It demonstrates a scalable alignment optimization that preserves clustering characteristics across models and enhances model training.
• Experimental results on image and text datasets validate SPEC's ability to expose distinct clustering patterns and improve embedding interoperability.

Spectral Pairwise Embedding Comparison: Towards Enhanced Interpretability and Alignment of Feature Embeddings

The development and evaluation of feature embeddings is a critical area in machine learning, particularly as they play a foundational role in representing raw inputs, such as images and text, into semantically meaningful feature spaces. The study "Towards an Explainable Comparison and Alignment of Feature Embeddings" introduces the Spectral Pairwise Embedding Comparison (SPEC) framework, aimed at providing an interpretable approach to assessing and aligning different embedding models.

Overview of the SPEC Framework

The SPEC framework addresses two primary objectives: comparing embeddings to understand the cluster differences in data they represent, and aligning them to preserve clustering characteristics across models. The paper critiques the traditional reliance on performance metrics for embedding comparison, emphasizing the need for interpretations that reveal how embeddings distinctively cluster data. SPEC focuses on the eigendecomposition of the difference kernel matrix between two embeddings, which allows for identifying sample groups that are clustered differently by each embedding.

Methodology

SPEC employs kernel functions to transform embeddings into similarity matrices, performing eigendecomposition on the difference of these matrices to isolate clusters uniquely identified by one embedding but not the other. This approach ensures:

• Scalability: The framework supports linear computational growth relative to sample size, which is essential for handling large datasets effectively.
• Alignment Optimization: An alignment routine is proposed to minimize disparities in clustering across embeddings. This involves solving an optimization problem that integrates a SPEC-based distance measure (SPEC-diff) with conventional training losses, enabling the alignment of embedding spaces effectively during model training.

Experimental Evaluation

The paper presents rigorous evaluation of SPEC with several experiments comparing well-known image and text embeddings, such as CLIP, DINOv2, and RoBERTa, across datasets including ImageNet, MS-COCO, and custom configurations with text overlays. Results consistently indicate SPEC’s prowess in exposing embedding-induced clustering differences, such as the tendency of CLIP to prioritize overlaid text in image clustering.

Additionally, the SPEC-align approach demonstrated effectiveness in aligning embeddings to improve their representational convergence, as validated by aligning CLIP and DINOv2 and observing improved performance in visual tasks like image classification.

Results and Implications

The results reveal that SPEC can successfully identify clusters that are differently perceived by distinct embeddings, providing deeper insights into the functional nuances of these models beyond traditional performance metrics. The SPEC-align extension fosters improved interoperability between embeddings, potentially enhancing cross-application adaptability and fine-tuning strategies in multi-modality models.

The implications of this work are significant for both theoretical advancements and practical applications in machine learning:

• Interpretability: SPEC reflects a shift towards embedding assessments that prioritize interpretability, contributing to model transparency and debuggability.
• Embeddings Alignment: The successful alignment of embeddings can lead to richer, more coherent feature representations, supporting more accurate and efficient downstream task performances.
• Future Prospects in AI: There is potential for SPEC to evolve, incorporating unsupervised clustering mechanisms and extending to more complex multi-modal frameworks, thus broadening its applicability across diverse AI models.

In conclusion, the SPEC framework offers an insightful, computationally efficient strategy for comparing and aligning feature embeddings, fostering deeper interpretability and enhanced model adaptation. This contribution aligns with the growing emphasis on transparency and explainability in AI, paving the way for more informed and effective embedding strategies across various domains.