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The work introduces “tail quality” and the metric “X-Critical-Quality” to quantify constrained inference quality, analyzes why existing approaches fail, and calls for new tools and methodologies.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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in practice?","Question",{"text":63,"@type":64},"It shows that inference quality in real deployments follows a long-tail distribution, meaning quality fluctuations and occasional poor performance are likely under practical constraints.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Why can tail inference quality be especially harmful?",{"text":68,"@type":64},"In safety-critical and mission-critical tasks, a single low-quality inference can lead to irreversible or even fatal losses, since many applications have strict performance requirements.",{"name":70,"@type":61,"acceptedAnswer":71},"What new terms and metrics does the paper propose?",{"text":72,"@type":64},"The paper coins “tail quality” to characterize the essential requirement and proposes “X-Critical-Quality” to measure inference quality under specific 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Wanling Gaoa,b , Chunjie Luoa,b , Lei Wanga,b and Jianfeng Zhana,b,\u003C  \narXiv :2212 . 13925v1 [ cs .LG] 25 Dec 2022  \na Research Center for Advanced Computer Systems, Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, 100190, Beijing, China  \nb University of Chinese Academy of Sciences, No. 19 (A) Yuquan Road, Shijingshan District, 100049, Beijing, China  \n\n| ARTICLE INFO\u003Cbr>Keywords:\u003Cbr>Tail Quality\u003Cbr>Quality Fluctuation Artificial Intelligence Deep Learning Inference Inference Accuracy\u003Cbr>Inference Time | AB STRACT\u003Cbr>Practical applications employing deep learning must guarantee inference quality. However, we found that the inference quality of state-of-the-art and state-of-the-practice in practical applications has a long tail distribution. In the real world, many tasks have strict requirements for the quality of deep learning inference, such as safety-critical and mission-critical tasks. The fluctuation of inference quality seriously affects its practical applications, and the quality at the tail may lead to severe consequences. State-of-the-art and state-of-the-practice with outstanding inference quality designed and trained under loose constraints still have poor inference quality under constraints with practical application significance. On the one hand, the neural network models must be deployed on complex systems with limited resources. On the other hand, safetycritical and mission-critical tasks need to meet more metric constraints while ensuring high inference quality.\u003Cbr>We coin a new term,“tail quality,” to characterize this essential requirement and challenge. We also propose a new metric,“X-Critical-Quality,” to measure the inference quality under certain constraints. This article reveals factors contributing to the failure of using state-of-theart and state-of-the-practice algorithms and systems in real scenarios. Therefore, we call for establishing innovative methodologies and tools to tackle this enormous challenge. |\n| --- | --- |\n| 1. Introduction\u003Cbr>In the past few decades, with the great innovation of artificial intelligence (AI), deep learning has gradually become a critical technology that dominates the continuous development of daily products and services, such as autonomous driving [4, 9, 2], medical emergency management [7], and financial quantitative [1, 18] . Neural network models deployed in practical applications must guarantee the stability of their inference quality. However, under the restriction of various practical factors from software, hardware, and data, it is difficult for the deployed model to achieve the best inference quality level obtained during the training phase. It may even be impossible to deploy the model according to the original design. Many research fields are devoted to addressing these issues. For example, to mitigate the degradation of inference quality of deployed models on real-world data as much as possible, many domain generalization algorithms [21, 19] have been proposed to address this issue. Additionally, algorithms such as model compression [6] and collaborative inference [11, 15, 8] are proposed so that models can be deployed on resource-constrained or complex systems without losing the inference quality as much as possible. Worryingly, all these efforts have neglected a critical situation: how to avoid fluctuations and abnormalities of the inference quality of the model under the constraints of specific performance metrics. Moreover, this unstable and unpredictable inference quality in practical applications, especially in safety-critical and mission-critical tasks [16], will bring irreparable or fatal losses and consequences.\u003Cbr>In the real world, due to the characteristics of the neural network that are difficult to explain, the complexity of the software and hardware systems, and the particularity of the safety-critical and mission-critical tasks, the quality of products and","cbCairZ1kLlJavhf","https://ap.wps.com/l/cbCairZ1kLlJavhf","pdf",3981781,"English","# Introduction\n## Long-tail inference quality in real deployments\n## Constraints: resources and critical metrics\n## Proposed concepts and metrics","[{\"question\":\"What problem does the paper highlight about deep learning inference in practice?\",\"answer\":\"It shows that inference quality in real deployments follows a long-tail distribution, meaning quality fluctuations and occasional poor performance are likely under practical constraints.\"},{\"question\":\"Why can tail inference quality be especially harmful?\",\"answer\":\"In safety-critical and mission-critical tasks, a single low-quality inference can lead to irreversible or even fatal losses, since many applications have strict performance requirements.\"},{\"question\":\"What new terms and metrics does the paper propose?\",\"answer\":\"The paper coins “tail quality” to characterize the essential requirement and proposes “X-Critical-Quality” to measure inference quality under specific constraints.\"}]","Quality at the Tail - Tail Quality - X-Critical-Quality | PDF",1789642470,23]