CADS: Conformal Adaptive Decision System for Cost-Efficient Image Classification
1d ago
Quick Take
CADS optimizes image classification by dynamically routing samples to reduce costs and improve efficiency.
Key Points
- Introduces a multi-model algorithm for resource allocation.
- Utilizes conformal prediction for real-time uncertainty quantification.
- Achieves up to 12x lower computational costs than heavy models.
📖 Reader Mode
~2 min readAbstract:While high-capacity AI models have advanced state-of-the-art performance, their practical deployment is often hindered by high inference costs, environmental impact, and a "one-size-fits-all" approach that ignores varying sample complexity. In clinical settings for instance, the waste of computational resources on routine cases is a significant barrier to sustainable AI. In this paper, we introduce the Conformal Adaptive Decision System (CADS), a sequential multi-model algorithm designed to optimize resource allocation by efficiently sampling models based on the estimated data complexity. CADS leverages conformal prediction to quantify image uncertainty at runtime. CADS provides a mathematically grounded framework for balancing the cost-accuracy dilemma that dynamically routes samples through a model cascade, ranging from lightweight "Scout" models to high-capacity "Oracle" architectures. Validated on two datasets, CADS demonstrated superior efficiency and accuracy at a computational cost that can be up to 12 times lower than heavy-model inference. By accurately routing samples based on real-time complexity, CADS ensures high diagnostic reliability while drastically reducing the economic and environmental footprint of AI.
| Comments: | 6 pages, 2 figures, 1 table, Accepted at ICIP 2026 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.16401 [cs.CV] |
| (or arXiv:2605.16401v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16401 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tim Bary [view email]
[v1]
Tue, 12 May 2026 19:38:09 UTC (250 KB)
— Originally published at arxiv.org
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