{"title":"ShapeShop:通过互动实验来理解深度学习表征。","authors":"Fred Hohman, Nathan Hodas, Duen Horng Chau","doi":"10.1145/3027063.3053103","DOIUrl":null,"url":null,"abstract":"<p><p>Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as \"black-boxes\" due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user's data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.</p>","PeriodicalId":73006,"journal":{"name":"Extended abstracts on Human factors in computing systems. CHI Conference","volume":"2017 ","pages":"1694-1699"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3027063.3053103","citationCount":"23","resultStr":"{\"title\":\"ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation.\",\"authors\":\"Fred Hohman, Nathan Hodas, Duen Horng Chau\",\"doi\":\"10.1145/3027063.3053103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as \\\"black-boxes\\\" due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user's data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.</p>\",\"PeriodicalId\":73006,\"journal\":{\"name\":\"Extended abstracts on Human factors in computing systems. CHI Conference\",\"volume\":\"2017 \",\"pages\":\"1694-1699\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1145/3027063.3053103\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Extended abstracts on Human factors in computing systems. CHI Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3027063.3053103\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Extended abstracts on Human factors in computing systems. CHI Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3027063.3053103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation.
Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as "black-boxes" due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user's data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.