CKANs are measurably less efficient than standard CNNs, and on ImageNet the accuracy gap is large, but the paper's baseline and timing comparisons are not controlled.
Exploring Kolmogorov-Arnold networks for realistic image sharpness assessment
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abstract
Score prediction is crucial in evaluating realistic image sharpness based on collected informative features. Recently, Kolmogorov-Arnold networks (KANs) have been developed and witnessed remarkable success in data fitting. This study introduces the Taylor series-based KAN (TaylorKAN). Then, different KANs are explored in four realistic image databases (BID2011, CID2013, CLIVE, and KonIQ-10k) to predict the scores by using 15 mid-level features and 2048 high-level features. Compared to support vector regression, results show that KANs are generally competitive or superior, and TaylorKAN is the best one when mid-level features are used. This is the first study to investigate KANs on image quality assessment that sheds some light on how to select and further improve KANs in related tasks.
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Efficiency Bottlenecks of Convolutional Kolmogorov-Arnold Networks: A Comprehensive Scrutiny with ImageNet, AlexNet, LeNet and Tabular Classification
CKANs are measurably less efficient than standard CNNs, and on ImageNet the accuracy gap is large, but the paper's baseline and timing comparisons are not controlled.