Exploring Learning Complexity for Downstream Data Pruning

The over-parameterized pre-trained models pose a great challenge to fine-tuning with limited computation resources. An intuitive solution is to prune the less informative samples from the fine-tuning dataset. A series of training-based scoring functions are proposed to quantify the informativeness of the data subset but the pruning cost becomes non-negligible due to the heavy parameter updating. For efficient pruning, it is viable to adapt the similarity scoring function of geometric-based methods from training-based to training-free. However, we empirically show that …

challenge complexity computation cost cs.lg data data pruning dataset fine-tuning functions pre-trained models pruning resources samples scoring series solution training