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公开(公告)号：US20170220906A1
公开(公告)日：2017-08-03
申请号：US15488041
申请日：2017-04-14
Applicant: Google Inc.
Inventor： Samy Bengio , Jeffrey Adgate Dean , Quoc V. Le , Jonathon Shlens , Yoram Singer
CPC classification number: G06K9/723 , G06K9/00677 , G06K9/6212 , G06K9/72 , G06K2009/6213 , G06N7/005 , G06N20/00
Abstract: Systems and techniques are disclosed for labeling objects within an image. The objects may be labeled by selecting an option from a plurality of options such that each option is a potential label for the object. An option may have an option score associated with. Additionally, a relation score may be calculated for a first option and a second option corresponding to a second object in an image. The relation score may be based on a frequency, probability, or observance corresponding to the co-occurrence of text associated with the first option and the second option in a text corpus such as the World Wide Web. An option may be selected as a label for an object based on a global score calculated based at least on an option score and relation score associated with the option.
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公开(公告)号：US20160140435A1
公开(公告)日：2016-05-19
申请号：US14941454
申请日：2015-11-13
Applicant: Google Inc.
Inventor： Samy Bengio , Oriol Vinyals , Alexander Toshkov Toshev , Dumitru Erhan
CPC classification number: G06N3/0472 , G06F17/28 , G06N3/0454
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating descriptions of input images. One of the methods includes obtaining an input image; processing the input image using a first neural network to generate an alternative representation for the input image; and processing the alternative representation for the input image using a second neural network to generate a sequence of a plurality of words in a target natural language that describes the input image.
Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于产生输入图像的描述。 方法之一包括获取输入图像; 使用第一神经网络处理所述输入图像以生成所述输入图像的替代表示; 以及使用第二神经网络处理所述输入图像的替代表示，以生成描述所述输入图像的目标自然语言中的多个单词的序列。
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公开(公告)号：US09176988B2
公开(公告)日：2015-11-03
申请号：US13966737
申请日：2013-08-14
Applicant: Google Inc.
Inventor： Samy Bengio , Erik Murphy-Chutorian , Yangli Hector Yee , Charles J. Rosenberg
CPC classification number: G06F17/30256 , G06F17/30271 , G06K9/6223 , G06K9/6262
Abstract: Methods, systems, and apparatus, including computer program products, for identifying images relevant to a query are disclosed. An image search subsystem selects images to reference in image search results that are responsive to a query based on an image relevance model that is trained for the query. An independent image relevance model is trained for each unique query that is identified by the image search subsystem. The image relevance models can be applied to images to order image search results obtained for the query. Each relevance model is trained based on content feature values of images that are identified as being relevant to the query (e.g., frequently selected from the image search results) and images that are identified as being relevant to another unique query. The trained model is applied to the content feature values of all known images to generate an image relevance score that can be used to order search results for the query.
Abstract translation: 公开了用于识别与查询相关的图像的方法，系统和装置，包括计算机程序产品。 图像搜索子系统基于针对查询进行训练的图像相关性模型，在响应于查询的图像搜索结果中选择图像进行参考。 对由图像搜索子系统识别的每个唯一查询训练独立的图像相关性模型。 图像相关性模型可以应用于图像以订购为查询获得的图像搜索结果。 基于被识别为与查询相关（例如，从图像搜索结果中频繁选择）的图像的内容特征值以及被识别为与另一唯一查询相关的图像来训练每个相关性模型。 经过训练的模型被应用于所有已知图像的内容特征值，以生成可用于对查询进行搜索结果的图像相关性分数。
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公开(公告)号：US09087271B2
公开(公告)日：2015-07-21
申请号：US14455350
申请日：2014-08-08
Applicant: Google Inc.
Inventor： Gal Chechik , Samy Bengio , Varun Sharma
CPC classification number: G06K9/66 , G06F17/30247 , G06F17/30265 , G06K9/6212 , G06K9/6276
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying similar images. In some implementations, a method is provided that includes receiving a collection of images and data associated with each image in the collection of images; generating a sparse feature representation for each image in the collection of images; and training an image similarity function using image triplets sampled from the collection of images and corresponding sparse feature representations.
Abstract translation: 方法，系统和装置，包括编码在计算机存储介质上的用于识别相似图像的计算机程序。 在一些实施方式中，提供了一种方法，其包括接收图像集合中与图像集合中的每个图像相关联的图像和数据的集合; 为图像集合中的每个图像生成稀疏特征表示; 并使用从图像集合和相应的稀疏特征表示中采样的图像三元组训练图像相似度函数。
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公开(公告)号：US20150186793A1
公开(公告)日：2015-07-02
申请号：US14141803
申请日：2013-12-27
Applicant: GOOGLE INC.
Inventor： Sergey Ioffe , Samy Bengio
CPC classification number: G06N20/00
Abstract: A computer-implemented method can include receiving training data that includes a set of non-matching pairs and a set of matching pairs. The method can further include calculating a non-matching collision probability for each non-matching pair of the set of non-matching pairs and a matching collision probability for each matching pair of the set of matching pairs. The method can also include generating a machine learning model that includes a first threshold and a second threshold. An unknown item and a particular known item are classified as not matching when their collision probability is less than the first threshold, and as matching when their collision probability is greater than the second threshold. The first threshold and the second threshold can be selected based on a minimization of errors in classification of matching and non-matching pairs in the training data, and a maximization of a retrieval efficiency metric.
Abstract translation: 计算机实现的方法可以包括接收包括一组非匹配对和一组匹配对的训练数据。 所述方法还可以包括：计算所述一组非匹配对中的每个非匹配对的不匹配冲突概率以及所述一组匹配对中的每个匹配对的匹配冲突概率。 该方法还可以包括生成包括第一阈值和第二阈值的机器学习模型。 未知项目和特定已知项目当其冲突概率小于第一阈值时被分类为不匹配，并且当其冲突概率大于第二阈值时被匹配。 可以基于训练数据中的匹配和非匹配对的分类中的误差的最小化以及检索效率度量的最大化来选择第一阈值和第二阈值。
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Patent Agency Ranking
公开(公告)号：US20150161485A1
公开(公告)日：2015-06-11
申请号：US14455350
申请日：2014-08-08
Applicant: Google Inc.
Inventor： Gal Chechik , Samy Bengio , Varun Sharma
CPC classification number: G06K9/66 , G06F17/30247 , G06F17/30265 , G06K9/6212 , G06K9/6276
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying similar images. In some implementations, a method is provided that includes receiving a collection of images and data associated with each image in the collection of images; generating a sparse feature representation for each image in the collection of images; and training an image similarity function using image triplets sampled from the collection of images and corresponding sparse feature representations.
Abstract translation: 方法，系统和装置，包括编码在计算机存储介质上的用于识别相似图像的计算机程序。 在一些实施方式中，提供了一种方法，其包括接收图像集合中与图像集合中的每个图像相关联的图像和数据的集合; 为图像集合中的每个图像生成稀疏特征表示; 并使用从图像集合和相应的稀疏特征表示中采样的图像三元组训练图像相似度函数。
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公开(公告)号：US09858524B2
公开(公告)日：2018-01-02
申请号：US14941454
申请日：2015-11-13
Applicant: Google Inc.
Inventor： Samy Bengio , Oriol Vinyals , Alexander Toshkov Toshev , Dumitru Erhan
CPC classification number: G06N3/0472 , G06F17/28 , G06N3/0454
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating descriptions of input images. One of the methods includes obtaining an input image; processing the input image using a first neural network to generate an alternative representation for the input image; and processing the alternative representation for the input image using a second neural network to generate a sequence of a plurality of words in a target natural language that describes the input image.
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公开(公告)号：US09721214B1
公开(公告)日：2017-08-01
申请号：US15231534
申请日：2016-08-08
Applicant: Google Inc.
Inventor： Gregory S. Corrado , Kai Chen , Jeffrey A. Dean , Samy Bengio , Rajat Monga , Matthieu Devin
CPC classification number: G06N99/005 , G06K9/6256 , G06K9/6269 , G06N3/063 , G06N3/08 , G06N5/025 , G06N7/005 , G06N7/08
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a model using parameter server shards. One of the methods includes receiving, at a parameter server shard configured to maintain values of a disjoint partition of the parameters of the model, a succession of respective requests for parameter values from each of a plurality of replicas of the model; in response to each request, downloading a current value of each requested parameter to the replica from which the request was received; receiving a succession of uploads, each upload including respective delta values for each of the parameters in the partition maintained by the shard; and updating values of the parameters in the partition maintained by the parameter server shard repeatedly based on the uploads of delta values to generate current parameter values.
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公开(公告)号：US09412065B1
公开(公告)日：2016-08-09
申请号：US14817745
申请日：2015-08-04
Applicant: Google Inc.
Inventor： Gregory S. Corrado , Kai Chen , Jeffrey A. Dean , Samy Bengio , Rajat Monga , Matthieu Devin
CPC classification number: G06N99/005 , G06K9/6256 , G06K9/6269 , G06N3/063 , G06N3/08 , G06N5/025 , G06N7/005 , G06N7/08
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a model using parameter server shards. One of the methods includes receiving, at a parameter server shard configured to maintain values of a disjoint partition of the parameters of the model, a succession of respective requests for parameter values from each of a plurality of replicas of the model; in response to each request, downloading a current value of each requested parameter to the replica from which the request was received; receiving a succession of uploads, each upload including respective delta values for each of the parameters in the partition maintained by the shard; and updating values of the parameters in the partition maintained by the parameter server shard repeatedly based on the uploads of delta values to generate current parameter values.
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公开(公告)号：US09183226B2
公开(公告)日：2015-11-10
申请号：US14336692
申请日：2014-07-21
Applicant: Google Inc.
Inventor： Yangli Hector Yee , Samy Bengio , Charles J. Rosenberg , Erik Murphy-Chutorian
CPC classification number: G06F17/30253 , G06F17/30244 , G06F17/30864 , G06K9/4676 , G06K9/6256 , G06K9/66 , G06K2209/01
Abstract: An image classification system trains an image classification model to classify images relative to text appearing with the images. Training images are iteratively selected and classified by the image classification model according to feature vectors of the training images. An independent model is trained for unique n-grams of text. The image classification system obtains text appearing with an image and parses the text into candidate labels for the image. The image classification system determines whether an image classification model has been trained for the candidate labels. When an image classification model corresponding to a candidate label has been trained, the image classification subsystem classifies the image relative to the candidate label. The image is labeled based on candidate labels for which the image is classified as a positive image.
Abstract translation: 图像分类系统训练图像分类模型，以便相对于与图像一起出现的文本来分类图像。 根据训练图像的特征向量，通过图像分类模型迭代地选择和分类训练图像。 对独特的n克文本进行了独立的模型训练。 图像分类系统获得与图像一起出现的文本，并将文本解析为图像的候选标签。 图像分类系统确定是否针对候选标签训练了图像分类模型。 当对应于候选标签的图像分类模型已经被训练时，图像分类子系统将图像相对于候选标签进行分类。 基于将图像分类为正图像的候选标签来标记图像。
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