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Deep learning models often require large amounts of data for training,leading to increased costs. It is particularly challenging in medical imaging,i.e., gathering distributed data for centralized training, and meanwhile,obtaining quality labels remains a tedious job. Many methods have been proposedto address this issue in various training paradigms, e.g., continual learning,active learning, and federated learning, which indeed demonstrate certain formsof the data valuation process. However, existing methods are either overlyintuitive or limited to common clean/toy datasets in the experiments. In thiswork, we present two data valuation metrics based on Synaptic Intelligence andgradient norms, respectively, to study the redundancy in real-world image data.Novel online and offline data selection algorithms are then proposed viaclustering and grouping based on the examined data values. Our online approacheffectively evaluates data utilizing layerwise model parameter updates andgradients in each epoch and can accelerate model training with fewer epochs anda subset (e.g., 19%-59%) of data while maintaining equivalent levels ofaccuracy in a variety of datasets. It also extends to the offline coresetconstruction, producing subsets of only 18%-30% of the original. The codes forthe proposed adaptive data selection and coreset computation are available(https://github.com/ZhenyuTANG2023/data_selection).

Exploring Data Redundancy in Real-world Image Classification through Data Selection