Francesco Di Salvo
@francescodisalvo.bsky.social
PhD Student in Robust Machine Learning @ University of Bamberg
francescodisalvo05.github.io
francescodisalvo05.github.io
Reposted by Francesco Di Salvo
New paper alert!
Zero-Shot Coreset Selection: Efficient Pruning for Unlabeled Data
Training models requires massive amounts of labeled data. ZCore shows you that you need less labeled data to train good models.
Paper Link: arxiv.org/abs/2411.15349
GitHub Repo: github.com/voxel51/zcore
Zero-Shot Coreset Selection: Efficient Pruning for Unlabeled Data
Training models requires massive amounts of labeled data. ZCore shows you that you need less labeled data to train good models.
Paper Link: arxiv.org/abs/2411.15349
GitHub Repo: github.com/voxel51/zcore
December 9, 2024 at 4:59 PM
New paper alert!
Zero-Shot Coreset Selection: Efficient Pruning for Unlabeled Data
Training models requires massive amounts of labeled data. ZCore shows you that you need less labeled data to train good models.
Paper Link: arxiv.org/abs/2411.15349
GitHub Repo: github.com/voxel51/zcore
Zero-Shot Coreset Selection: Efficient Pruning for Unlabeled Data
Training models requires massive amounts of labeled data. ZCore shows you that you need less labeled data to train good models.
Paper Link: arxiv.org/abs/2411.15349
GitHub Repo: github.com/voxel51/zcore