24 citations · 28 across the 3 of their papers we have counts for
Showing 2019Show all
2 papers · 1 filter
cs.CR2019
REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited Data
Xinyun Chen, Wenxiao Wang, Chris Bender +4
Training deep neural networks from scratch could be computationally expensive and requires a lot of training data. Recent work has explored different watermarking techniques to pro…
cs.LG2019
Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?
Ruoxi Jia, Fan Wu, Xuehui Sun +6
Quantifying the importance of each training point to a learning task is a fundamental problem in machine learning and the estimated importance scores have been leveraged to guide a…