CORTEX: Concept-Oriented Token Explanation in Vector-Quantized Generative Model
Keywords: Vector-Quantized Generative Model, Explainability, Information Bottleneck
Abstract: Vector-Quantized Generative Models (VQGMs) have emerged as powerful tools for image generation. However, the key component of VQGMs---the codebook of discrete tokens---is still not well understood, e.g., which tokens are critical to generate an image of a certain concept? This paper introduces Concept-Oriented Token Explanation (CORTEX), a novel approach for interpreting VQGMs by identifying concept-specific token combinations. Our framework employs two methods: (1) a saliency-based method that analyzes token saliency value in individual images, and (2) an optimization-based method that explores the entire codebook to find globally relevant tokens. Experimental results demonstrate CORTEX's efficacy in providing clear explanations of token usage in the generative process, outperforming baselines across multiple pretrained VQGMs. CORTEX not only improves VQGM transparency but also enables tasks such as targeted image editing, offering valuable insights into the model's internal representations.
Primary Area: interpretability and explainable AI
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Submission Number: 2258
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