Just Leaf It: Accelerating Diffusion Classifiers with Hierarchical Class Pruning

• URL: http://arxiv.org/abs/2411.12073v1
• Date: Mon, 18 Nov 2024 21:34:05 GMT
• Title: Just Leaf It: Accelerating Diffusion Classifiers with Hierarchical Class Pruning
• Authors: Arundhati S. Shanbhag, Brian B. Moser, Tobias C. Nauen, Stanislav Frolov, Federico Raue, Andreas Dengel,
• Abstract summary: We present a Hierarchical Diffusion (HDC) that exploits the inherent hierarchical label structure of a dataset. HDC can accelerate inference by up to 60% while maintaining and, in some cases, improving classification accuracy. Our work enables a new control mechanism of the trade-off between speed and precision, making diffusion-based classification more viable for real-world applications.
• Score: 8.209660505275872
• License:
• Abstract: Diffusion models, known for their generative capabilities, have recently shown unexpected potential in image classification tasks by using Bayes' theorem. However, most diffusion classifiers require evaluating all class labels for a single classification, leading to significant computational costs that can hinder their application in large-scale scenarios. To address this, we present a Hierarchical Diffusion Classifier (HDC) that exploits the inherent hierarchical label structure of a dataset. By progressively pruning irrelevant high-level categories and refining predictions only within relevant subcategories, i.e., leaf nodes, HDC reduces the total number of class evaluations. As a result, HDC can accelerate inference by up to 60% while maintaining and, in some cases, improving classification accuracy. Our work enables a new control mechanism of the trade-off between speed and precision, making diffusion-based classification more viable for real-world applications, particularly in large-scale image classification tasks.

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