IEEE 2841-2022

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IEEE Recommended Practice for Framework and Process for Deep Learning Evaluation (Approved Draft)

Published By	Publication Date	Number of Pages
IEEE	2022

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Description

New IEEE Standard – Active. The recommendations on evaluating and improving algorithm reliability for shortening the development cycle of deep learning algorithms and improving the quality of software systems based on deep learning algorithms are defined in this document. An assessment index system and corresponding assessment process are specified in this document.

PDF Catalog

PDF Pages	PDF Title
1	IEEE Std 2841™-2022 Front cover
2	Title page
4	Important Notices and Disclaimers Concerning IEEE Standards Documents
8	Participants
10	Introduction
11	Contents
13	1. Overview 1.1 Scope 1.2 General 1.3 Word usage
14	2. Normative references 3. Definitions
15	4. Assessment index system 4.1 Tables of the assessment index system 4.2 Correctness of algorithm function implementation 4.3 Correctness of code implementation
17	4.4 Effect of target function 4.5 Effect of training data sets 4.6 Effect of adversarial examples
18	4.7 Effect of dependency on hardware and software platforms 4.8 Effect of environmental data 5. Assessment process 5.1 Overview 5.2 Determination of reliability targets
21	5.3 Selection of assessment indexes 5.4 Assessment criteria 5.5 Assessment at all stages 5.6 Assessment conclusion
22	6. Assessment of the demand stage 6.1 Overview 6.2 Prerequisites 6.3 Input 6.4 Critical activities
23	6.5 Output 7. Assessment of the design stage 7.1 Overview 7.2 Prerequisites 7.3 Input
24	7.4 Critical activities 7.5 Output 8. Assessment of the implementation stage 8.1 Overview 8.2 Prerequisites
25	8.3 Input 8.4 Critical activities 8.5 Output
26	9. Assessment of the operation stage 9.1 Overview 9.2 Prerequisites 9.3 Input 9.4 Critical Activities
27	9.5 Output
28	Annex A (informative) Selection rules for reliability assessment indexes of deep learning algorithms
31	Annex B (informative) Bibliography
32	Back cover