Large-scale Dataset Pruning with Dynamic Uncertainty

arXiv:2306.05175v2 Announce Type: replace
Abstract: The state of the art of many learning tasks, e.g., image classification, is advanced by collecting larger datasets and then training larger models on them. As the outcome, the increasing computational cost is becoming unaffordable. In this paper, we investigate how to prune the large-scale datasets, and thus produce an informative subset for training sophisticated deep models with negligible performance drop. We propose a simple yet effective dataset pruning method by exploring both the prediction …

arxiv cs.cv cs.lg dataset dynamic pruning scale type uncertainty