中图分类号：TP391 文献标志码：A DOI: 10.16157/j.issn.0258-7998.234772
中文引用格式： 王绎翔. 用于主动学习的时序特征融合预测损失网络[J]. 电子技术应用，2024，50(6)：10-17.
英文引用格式： Wang Yixiang. Temporal feature fusion learning loss model for active learning[J]. Application of Electronic Technique，2024，50(6)：10-17.

Temporal feature fusion learning loss model for active learning

Abstract： Using active learning models to select the most valuable data points for annotation is one way that deep learning reduces the amount of labeled data required. Prediction loss models are a type of task-agnostic active learning models that perform well across multiple tasks. However, these models are not end-to-end models, and changing input features can lead to input bias during the training of the loss prediction network. This paper proposes a temporal feature fusion prediction loss model to address the issue of input bias in such models. Experiments demonstrate that the method proposed in this paper achieves an average performance improvement of approximately 1.5% across various tasks compared to previous state-of-the-art methods, and an average improvement of 5% compared to the original prediction loss model.

Key words : active learning；deep learning；learning loss；time series