Data Fine-Pruning: A Simple Way to Accelerate Neural Network Training

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11276)


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.


Deep Neural Network Data pruning SGD Acceleration 



This work is partially supported by the National Key R&D Program of China 2018YFB1003201 and Guangdong Pre-national Project 2014GKXM054.


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Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.The University of WarwickCoventryUK
  2. 2.Shenzhen UniversityShenzhenPeople’s Republic of China

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