CADS: Conformal Adaptive Decision System for Cost-Efficient Image Classification

TL;DR

CADS is a sequential multi-model system that uses conformal prediction to adaptively route image samples through models of varying complexity, significantly reducing inference costs while maintaining high accuracy.

Contribution

The paper introduces CADS, a novel conformal prediction-based framework for dynamic model routing that improves cost-efficiency in image classification tasks.

Findings

CADS reduces computational cost by up to 12 times compared to heavy-model inference.

CADS maintains high accuracy and reliability through real-time complexity-based routing.

Validated on two datasets, CADS outperforms traditional static models in efficiency.

Abstract

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.