{"title":"深度学习:好的、坏的和丑的。","authors":"T. Serre","doi":"10.1146/annurev-vision-091718-014951","DOIUrl":null,"url":null,"abstract":"Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision-giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems. Expected final online publication date for the Annual Review of Vision Science, Volume 5 is September 16, 2019. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48658,"journal":{"name":"Annual Review of Vision Science","volume":" ","pages":""},"PeriodicalIF":5.0000,"publicationDate":"2019-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/annurev-vision-091718-014951","citationCount":"173","resultStr":"{\"title\":\"Deep Learning: The Good, the Bad, and the Ugly.\",\"authors\":\"T. Serre\",\"doi\":\"10.1146/annurev-vision-091718-014951\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision-giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems. Expected final online publication date for the Annual Review of Vision Science, Volume 5 is September 16, 2019. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.\",\"PeriodicalId\":48658,\"journal\":{\"name\":\"Annual Review of Vision Science\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2019-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1146/annurev-vision-091718-014951\",\"citationCount\":\"173\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual Review of Vision Science\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1146/annurev-vision-091718-014951\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Review of Vision Science","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1146/annurev-vision-091718-014951","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision-giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems. Expected final online publication date for the Annual Review of Vision Science, Volume 5 is September 16, 2019. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
期刊介绍:
The Annual Review of Vision Science reviews progress in the visual sciences, a cross-cutting set of disciplines which intersect psychology, neuroscience, computer science, cell biology and genetics, and clinical medicine. The journal covers a broad range of topics and techniques, including optics, retina, central visual processing, visual perception, eye movements, visual development, vision models, computer vision, and the mechanisms of visual disease, dysfunction, and sight restoration. The study of vision is central to progress in many areas of science, and this new journal will explore and expose the connections that link it to biology, behavior, computation, engineering, and medicine.