[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117776-en":3,"doc-seo-117776-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117776,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine Learning for Synthetic Data Generation - A Review","Machine Learning for Synthetic Data Generation: A Review surveys machine learning–based approaches for creating synthetic data when real-world data is scarce, low quality, difficult to access, or constrained by privacy, safety, and regulations. The review organizes work by application domains such as computer vision, speech, natural language, healthcare, and business; by modeling techniques including neural network architectures and deep generative models; and by cross-cutting concerns of privacy and fairness. It also identifies key challenges, evaluates open problems, and outlines future research directions.","arXiv :2302 .04062v2 [ cs .LG] 29 Mar 2023  \nMachine Learning for Synthetic Data Generation: A Review  \nYINGZHOU LU, Virginia Tech, USA HUAZHENG WANG, Oregon State University, USA WENQI WEI∗ , Fordham University, USA  \nData plays a crucial role in machine learning. However, in real-world applications, there are several problems with data, e.g., data are of low quality; a limited number of data points lead to under-fitting of the machine learning model; it is hard to access the data due to privacy, safety and regulatory concerns. Synthetic data generation offers a promising new avenue, as it can be shared and used in ways that real-world data cannot. This paper systematically reviews the existing works that leverage machine learning models for synthetic data generation. Specifically, we discuss the synthetic data generation works from several perspectives: (i) applications, including computer vision, speech, natural language, healthcare, and business; (ii) machine learning methods, particularly neural network architectures and deep generative models; (iii) privacy and fairness issue. In addition, we identify the challenges and opportunities in this emerging field and suggest future research directions.  \nACM Reference Format:  \nYingzhou Lu, Huazheng Wang, and Wenqi Wei∗ . 2022. Machine Learning for Synthetic Data Generation: A Review. 1, 1 (March 2022), 18 pages. [https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX)  \n1 INTRODUCTION  \nMachine learning empowers intelligent computer systems to autonomously solve tasks and is pushing boundaries for industry innovations [1]. By combining high-performance computing, modern modeling, and simulations, machine learning has become an essential tool for handling and analyzing vast amounts of data [2, 3] .  \nHowever, we should note that machine learning does not always solve the problem or provides the best solution. Even though most researchers and experts agree that artificial intelligence is in its golden age today, there are still many obstacles to overcome when developing and applying machine learning technology [4] .  \nCollecting and annotating data is a time-consuming and costly process [5], and also brings a lot of issues. As machine learning relies heavily on data, the hurdles, and challenges including:  \n• Data quality. Data quality is one of the biggest challenges facing machine learning professionals. When data is of poor quality, the model can make incorrect or inaccurate predictions due to confusion [6] .  \n• Data scarcity. A significant part of the problem of modern AI stems from insufficient data: either the number of datasets available is too small, or manual labeling is prohibitively expensive [7] .  \n∗ Corresponding author.  \nAuthors’ addresses: Yingzhou Lu, Virginia Tech, 900 N Glebe Rd, Ballston, VA, USA, [lyz66@vt.edu](lyz66@vt.edu); Huazheng Wang, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR, USA, [huazheng.wang@oregonstate.edu](huazheng.wang@oregonstate.edu); Wenqi Wei ∗ , Fordham University, 113 W 60 St, New York, NY, USA, [wenqiwei@fordham.edu](wenqiwei@fordham.edu).  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or toredistribute to lists, [requires prior specific permission and/or a fee. Request permissions from permissions@acm.org](requires prior specific permission and/or a fee. Request permissions from permissions@acm.org).  \n© 2022 Association for Computing Machinery.  \nManuscript submitted to ACM  \nManuscript submitted to ACM 1  \n2  \nFig. 1. Synthetic data generation.  \n• Data privacy. There are many areas in which dat","cbCaiouuNkzgb8kW","https://ap.wps.com/l/cbCaiouuNkzgb8kW","pdf",769043,1,18,"English","en",105,"# Introduction\n## Data challenges and motivation\n## Synthetic data definition and use cases\n# Contributions and scope\n## Roadmap for the field\n## Application domains\n## Modeling methods\n## Privacy and fairness","[{\"question\":\"Why is synthetic data generation needed in machine learning?\",\"answer\":\"Real-world datasets can be low quality, limited in size, and hard to access due to privacy, safety, and regulatory constraints. Synthetic data helps when real data is unavailable or must remain private.\"},{\"question\":\"How does the review categorize synthetic data generation works?\",\"answer\":\"It structures the literature by application domains, by machine learning methods—especially neural network architectures and deep generative models—and by privacy and fairness issues.\"},{\"question\":\"What privacy and fairness concerns are discussed for synthetic data?\",\"answer\":\"Synthesized data may still leak sensitive information, and biases embedded in real-world data can be inherited. The review discusses current technical advances and their limitations in protecting privacy and maintaining fairness.\"}]","Machine Learning for Synthetic Data Generation - A Review | PDF",1785679495,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-synthetic-data-generation-a-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-synthetic-data-generation-a-review/117776/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is synthetic data generation needed in machine learning?","Question",{"text":75,"@type":76},"Real-world datasets can be low quality, limited in size, and hard to access due to privacy, safety, and regulatory constraints. Synthetic data helps when real data is unavailable or must remain private.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the review categorize synthetic data generation works?",{"text":80,"@type":76},"It structures the literature by application domains, by machine learning methods—especially neural network architectures and deep generative models—and by privacy and fairness issues.",{"name":82,"@type":73,"acceptedAnswer":83},"What privacy and fairness concerns are discussed for synthetic data?",{"text":84,"@type":76},"Synthesized data may still leak sensitive information, and biases embedded in real-world data can be inherited. 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