Name
Abstract | ||
|---|---|---|
The training process of a neural network is the most time-consuming procedure before being deployed to applications. In this paper, we investigate the loss trend of the training data during the training process. We find that given a fixed set of hyper-parameters, pruning specific types of training data can reduce the time consumption of the training process while maintaining the accuracy of the neural network. We developed a data fine-pruning approach, which can monitor and analyse the loss trend of training instances at real-time, and based on the analysis results, temporarily pruned specific instances during the training process basing on the analysis. Furthermore, we formulate the time consumption reduced by applying our data fine-pruning approach. Extensive experiments with different neural networks are conducted to verify the effectiveness of our method. The experimental results show that applying the data fine-pruning approach can reduce the training time by around 14.29% while maintaining the accuracy of the neural network. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-05677-3_10 | NPC |
Field | DocType | Citations |
Training set,Computer science,Acceleration,Artificial intelligence,Artificial neural network,Machine learning,Distributed computing,Pruning | Conference | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junyu Li | 1 | 0 | 1.35 |
| Ligang He | 2 | 542 | 56.73 |
| Shenyuan Ren | 3 | 0 | 1.69 |
| Rui Mao | 4 | 2 | 5.43 |