Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting

Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting

In software engineering, deep learning models are increasingly deployed for critical tasks such as bug detection and code review. However, overfitting remains a challenge that affects the quality, reliability, and trustworthiness of software systems that utilize deep learning models. Overfitting can...

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Bibliographic Details
Main Authors: Hao Li, Gopi Krishnan Rajbahadur, Dayi Lin, Cor-Paul Bezemer, Zhen Ming Jiang
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Software engineering
deep learning
machine learning
overfitting
training history
early stopping
Online Access:https://ieeexplore.ieee.org/document/10533733/

Internet

https://ieeexplore.ieee.org/document/10533733/

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