arXiv Machine Learning By Mikael Turkoglu, Tim Bary, Vincent Thielens, Manon Dausort, Beno\^it Macq

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

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arXiv:2605. 16401v2 Announce Type: replace-cross 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.

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arXiv Machine Learning
1d ago

Low-Budget Active Learning through Entropic Optimal Transport

The paper introduces a low-budget active learning approach that selects a small coreset of data points for training high-accuracy models, particularly useful when labeling is expensive, such as in medical contexts. It uses features from a pretrained self-supervised model and applies entropic optimal transport—specifically the Sinkhorn divergence—as the selection criterion, enabling dimension-free sample complexity and efficient gradient-based optimization. The method combines gradient-based candidate generation with a swap-based local search, achieving superior performance over existing heuristics on image and medical datasets.

By Rim Hajal, Mathieu Besan\c{c}on, J\'er\^ome Malick