L2T-DFM: Learning to Teach with Dynamic Fused Metric

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhaoyang Hai, Liyuan Pan, Xiabi Liu, Mengqiao Han
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引用次数: 0

Abstract

The loss function plays a crucial role in the construction of machine learning algorithms. Employing a teacher model to set loss functions dynamically for student models has attracted attention. In existing works, (1) the characterization of the dynamic loss suffers from some inherent limitations, ie, the computational cost of loss networks and the restricted similarity measurement handcrafted loss functions; and (2) the states of the student model are provided to the teacher model directly without integration, causing the teacher model to underperform when trained on insufficient amounts of data. To alleviate the above-mentioned issues, in this paper, we select and weigh a set of similarity metrics by a confidence-based selection algorithm and a temporal teacher model to enhance the dynamic loss functions. Subsequently, to integrate the states of the student model, we employ statistics to quantify the information loss of the student model. Extensive experiments demonstrate that our approach can enhance student learning and improve the performance of various deep models on real-world tasks, including classification, object detection, and semantic segmentation scenarios.
L2T-DFM:利用动态融合指标学习教学
损失函数在构建机器学习算法中起着至关重要的作用。利用教师模型为学生模型动态设置损失函数已引起人们的关注。在现有的研究中,(1) 动态损失的表征存在一些固有的局限性,即损失网络的计算成本和手工制作的损失函数的相似性测量受限;(2) 学生模型的状态未经整合就直接提供给教师模型,导致教师模型在训练数据量不足时表现不佳。为了缓解上述问题,本文通过基于置信度的选择算法和时态教师模型来选择和权衡一组相似度指标,从而增强动态损失函数。随后,为了整合学生模型的状态,我们采用统计方法来量化学生模型的信息损失。广泛的实验证明,我们的方法可以增强学生的学习能力,并提高各种深度模型在实际任务中的性能,包括分类、物体检测和语义分割等场景。
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来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
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