王弘业, 钱权, 武星. 基于参数惩罚和经验回放的材料吸声系数回归增量学习[J]. 工程科学学报, 2023, 45(7): 1225-1231. DOI: 10.13374/j.issn2095-9389.2022.05.03.006
引用本文: 王弘业, 钱权, 武星. 基于参数惩罚和经验回放的材料吸声系数回归增量学习[J]. 工程科学学报, 2023, 45(7): 1225-1231. DOI: 10.13374/j.issn2095-9389.2022.05.03.006
WANG Hong-ye, QIAN Quan, WU Xing. Incremental learning of material absorption coefficient regression based on parameter penalty and experience replay[J]. Chinese Journal of Engineering, 2023, 45(7): 1225-1231. DOI: 10.13374/j.issn2095-9389.2022.05.03.006
Citation: WANG Hong-ye, QIAN Quan, WU Xing. Incremental learning of material absorption coefficient regression based on parameter penalty and experience replay[J]. Chinese Journal of Engineering, 2023, 45(7): 1225-1231. DOI: 10.13374/j.issn2095-9389.2022.05.03.006

基于参数惩罚和经验回放的材料吸声系数回归增量学习

Incremental learning of material absorption coefficient regression based on parameter penalty and experience replay

  • 摘要: 材料数据具有分批次、分阶段制备的特点,并且不同批次数据的分布也不同,而神经网络按批次学习材料数据时会存在平均准确率随批次下降的问题,这为人工智能应用于材料领域带来极大的挑战。为解决这个问题,将增量学习应用于材料数据的学习上,通过分析模型参数的变化,建立了参数惩罚机制以限制模型在学习新数据时对新数据过拟合的现象;通过增强样本空间多样性,提出经验回放方法应用于增量学习,将新数据与从缓存池中采样得到的旧数据进行联合训练。进一步地,将所提方法分别应用在材料吸声系数回归和图像分类任务上,实验结果表明采用增量学习方法后,平均准确率分别提升了45.93%和2.62%,平均遗忘率分别降低了2.25%和7.54%。除此之外,还分析了参数惩罚和经验回放方法中具体参数对平均准确率的影响, 结果显示平均准确率随着回放比例的增大而增大,随着惩罚系数的增大先增大后减小。综上所述,本文提出的方法能够跨模态、任务进行学习,且参数设置灵活,可以根据不同环境和任务进行变动,为材料数据的增量学习提供了可行的方案。

     

    Abstract: Material data are prepared in batches and stages, and data distribution in different batches varies. However, the average accuracy of neural networks declines when learning material data by batch, resulting in great challenges to the application of artificial intelligence in the materials field. Therefore, an incremental learning framework based on parameter penalty and experience replay was applied to learn streaming data. The average accuracy decline is due to two reasons: sudden variations of model parameters and a quite homogeneous sample feature space. By analyzing the model parameter variation, a mechanism of parameter penalty was established to limit the phenomenon of model parameters fitting toward new data when the model learns new data. The penalty strength of the parameters can be dynamically adjusted according to the speed of parameter change. The faster the speed of parameter changes, the higher the penalty strength, and vice versa, the lower the penalty strength. To enhance sample diversity, experience replay methods were proposed, which train the new and old data obtained by sampling from the cache pool. At the end of each incremental task, the incremental data were sampled and used for the update of the cache pool. Specifically, random sampling was adopted for the joint training, whereas reservoir sampling was used for the update of the cache pool. Further, the proposed methods (i.e., experience replay and parameter penalty) were applied to the material absorption coefficient regression and image classification tasks, respectively. The experimental results indicate that experience replay was more effective than parameter penalty, but the best results were obtained when both methods were used. Specifically, when both methods were used, the average accuracy of the benchmark increased by 45.93% and 2.62% and reduced the average forgetting rate by 86.60% and 67.20%, respectively. A comparison with existing methods reveals that our approach is more competitive. Additionally, the effects of specific parameters on the average accuracy were analyzed for both methods. The results indicate that the average accuracy increases with the proportion of experience replay and increases and then decreases when the penalty factor increases. In general, our approach is not limited by data modalities and learning tasks and can perform incremental learning on tabular or image data, regression, or classification tasks. Further, owing to the quite flexible parameter settings, it can be adapted to different environments and tasks.

     

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