Oct 28, 2022 · We evaluate CCS on five datasets and show that, at high pruning rates (e.g., 90%), it achieves significantly better accuracy than previous SOTA ...
Feb 1, 2023 · It performs better than a SOTA method (AUM) at high pruning rates, but underperforms random sampling. Beating random selection is crucial for ...
One-shot coreset selection aims to select a representative subset of the training data, given a pruning rate, that can later be used to train future models ...
A novel metric to measure the coverage of a dataset on a specific distribution by extending the classical geometric set cover problem to a distribution ...
Mar 6, 2023 · We find that CCS overcomes catastrophic accuracy drop at high pruning rates, outperforming. SOTA methods by a significant margin, based on the ...
Coverage-centric Coreset Selection (CCS) is a one-shot coreset selection algorithm jointly considering overall data coverage upon a distribution as well as the ...
We evaluate CCS on four datasets and show that they achieve significantly better accuracy than state-of-the-art coreset selection methods as well as random ...
Duration: 18:35
Posted: Nov 29, 2022
Posted: Nov 29, 2022
May 1, 2023 · One-shot coreset selection aims to select a subset of the training data, given a pruning rate, that can achieve high accuracy for models ...
Coverage-centric coreset selection for high pruning rates. H Zheng, R Liu, F Lai, A Prakash. International Conference on Learning Representations (ICLR) 2023 ...