3 accepted papers
The rapid growth of modern training datasets has significantly increased computational cost, motivating dataset pruning(DP) methods which retain only a subset of informative samples to reduce training cost. Existing pruning criteria typically rely on either intrinsic signals that assess samples inde…
The ever-growing size of training datasets enhances the generalization capability of machine learning models but also incurs exorbitant computational costs. Existing data pruning approaches aim to accelerate training by removing those less important samples. However, they often rely on gradients or…
In recent years, semantic segmentation has flourished in various applications. However, the high computational cost remains a significant challenge that hinders its further adoption. The filter pruning method for structured network slimming offers a direct and effective solution for the reduction o…