FATS:基于特征分布分析的深度学习增强测试选择

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Li Li;Chuanqi Tao;Hongjing Guo;Jingxuan Zhang;Xiaobing Sun
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引用次数: 0

摘要

深度学习已被应用于不同领域的许多应用中。然而,测试数据和训练数据之间的分布偏移是影响深度神经网络(DNN)质量的一个主要因素。为解决这一问题,现有研究主要侧重于通过使用标注测试数据对 DNN 模型进行再训练来增强 DNN 模型。然而,标注测试数据的成本很高,严重降低了 DNN 测试的效率。为了解决这个问题,测试选择策略性地选择了一小部分测试数据进行标注。遗憾的是,现有的测试选择方法很少关注数据分布的变化。为了解决这个问题,本文提出了一种测试选择方法,名为基于特征分布分析的测试选择(FATS)。FATS 分析测试数据和训练数据的分布,然后采用学习排序(一种解决排序任务的有监督机器学习)来智能地结合分析结果进行测试选择。我们对流行的数据集和 DNN 模型进行了实证研究,并将 FATS 与七种测试选择方法进行了比较。实验结果表明,FATS 有效地缓解了分布偏移的影响,在 DNN 模型增强方面的平均准确率提高了 19.6%$\sim$69.7%,优于所比较的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FATS: Feature Distribution Analysis-Based Test Selection for Deep Learning Enhancement
Deep Learning has been applied to many applications across different domains. However, the distribution shift between the test data and training data is a major factor impacting the quality of deep neural networks (DNNs). To address this issue, existing research mainly focuses on enhancing DNN models by retraining them using labeled test data. However, labeling test data is costly, which seriously reduces the efficiency of DNN testing. To solve this problem, test selection strategically selected a small set of tests to label. Unfortunately, existing test selection methods seldom focus on the data distribution shift. To address the issue, this paper proposes an approach for test selection named Feature Distribution Analysis-Based Test Selection (FATS). FATS analyzes the distributions of test data and training data and then adopts learning to rank (a kind of supervised machine learning to solve ranking tasks) to intelligently combine the results of analysis for test selection. We conduct an empirical study on popular datasets and DNN models, and then compare FATS with seven test selection methods. Experiment results show that FATS effectively alleviates the impact of distribution shifts and outperforms the compared methods with the average accuracy improvement of 19.6% $\sim$ 69.7% for DNN model enhancement.
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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