.png){kind=icon}
.png){kind=icon}
Return
Data Pruning: Redundant, Problematic, and Interdependent Samples
.png){kind=icon}
PRE
AI
DOI：10.1007/978-3-032-11733-5_12.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The performance of deep learning models is affected by not only data quantity but also data quality. Data pruning is a process by which practitioners can reduce the size of a dataset by only keeping the most important training data points, thereby achieving similar test set performance. We empirically investigate two popular data pruning methods under noisy and noiseless conditions and show that these methods fail in the presence of significant label noise. We highlight that the success of data pruning is distinctly affected by three factors: redundancy in the dataset, the presence of problematic samples, and interdependence between samples. We perform a detailed investigation on commonly used benchmark classification datasets and neural network architectures. We find that our observations are consistent across data distributions and training protocols.
Keywords:
Data pruning
Label noise
Deep learning
Journal
A
IF:
0
Papers:
35
Citations:
0