  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.
