Deep learning models require an enormous amount of data for training. However, recently there is a shift in machine learning from model-centric to data-centric approaches. In data-centric approaches, the focus is to refine and improve the quality of the data to improve the learning performance of the models rather than redesigning model architectures. In this paper, we propose CLIP i.e., Curriculum Learning with Iterative data Pruning. CLIP combines two data-centric approaches i.e., curriculum learning and dataset pruning to improve the model learning accuracy and convergence speed. The proposed scheme applies loss-aware dataset pruning to iteratively remove the least significant samples and progressively reduces the size of the effective dataset in the curriculum learning training. Extensive experiments performed on crowd density estimation models validate the notion behind combining the two approaches by reducing the convergence time and improving generalization. To our knowledge, the idea of data pruning as an embedded process in curriculum learning is novel.

翻译：深度学习模型需要大量数据进行训练。然而，近年来机器学习正从模型中心方法向数据中心方法转变。在数据中心方法中，重点是通过精炼和提升数据质量来改善模型的学习性能，而非重新设计模型架构。本文提出了CLIP方法，即迭代数据剪枝的课程学习（Curriculum Learning with Iterative data Pruning）。CLIP融合了两种数据中心方法——课程学习与数据集剪枝，以提高模型的学习精度和收敛速度。所提出的方案利用基于损失的数据集剪枝，迭代移除最不重要的样本，并在课程学习训练中逐步缩减有效数据集的大小。在人群密度估计模型上开展的大量实验验证了结合这两种方法的可行性，能够减少收敛时间并提升泛化能力。据我们所知，将数据剪枝作为课程学习中的嵌入式过程是一个新颖的思路。