翻訳と辞書 Words near each other ・ Multiple Epidermal Growth Factor-like Domains 8 ・ Multiple epiphyseal dysplasia ・ Multiple Equivalent Simultaneous Offers ・ Multiple evanescent white dot syndrome ・ Multiple exciton generation ・ Multiple exposure ・ Multiple factor analysis ・ Multiple familial trichoepithelioma ・ Multiple follicular unit grafts ・ Multiple frames interface ・ Multimodal Architecture and Interfaces ・ Multimodal browser ・ Multimodal Caucedo Port ・ Multimodal distribution ・ Multimodal interaction ・ Multimodal learning ・ Multimodal logic ・ Multimodal search ・ Multimodal therapy ・ Multimodal transport ・ Multimodality ・ Multimode manual transmission ・ Multimonica ・ Multinational ・ Multinational Centre of Excellence for Mountain Warfare ・ Multinational Character Set ・ Multinational corporation ・ Multinational Corps Northeast ・ Multinational Division Central ・ Multinational Division Central-South Dictionary Lists mini英和辞書 mini和英辞書 Webster 1913 Latin-English FOLDOC Wikipedia English ウィキペディア

翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Multimodal learning ： ウィキペディア英語版 Multimodal learning The information in real world usually comes as different modalities. For example, images are usually associated with tags and text explanations; texts contain images to more clearly express the main idea of the article. Different modalities are characterized by very different statistical properties. For Instance, image is usually represented as pixel intensities or outputs of feature extractors, while texts are represented as discrete word count vectors. Due to distinct statistical properties of different information resources, it is very important to discover the relationship between different modalities. Multimodal learning is a good model to represent the joint representations of different modalities. The multimodal learning model is also capable to fill missing modality given the observed ones. The multimodal learning model combines two Deep Boltzmann Machines each corresponds to one modality. An additional hidden layer is placed on top of the two Boltzmann Machines to give the joint representation. ==Motivation== A lot of models/algorithms have been implemented to retrieve and classify a certain type of data, e.g. image or text. However, data usually come with different modalities which carry different information. For example, it is very common to caption an image to convey the information not presented by this image. Similarly, sometimes it is more straightforward to use a image to description the information which may not be obvious from texts. As a results, if some different words appear in similar images, these words are very likely used to describe the same thing. Conversely, if some words are used in different images, these images may represent the same object. Thus, it is important to invite a novel model which is able to jointly represent the information such that the model can capture the correlation structure between different modalities. Moreover, it should also be able to recover missing modalities given observed ones, e.g. predicting possible image object according to text description. The Multimodal Deep Boltzmann Machine model satisfies the above purposes. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Multimodal learning」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.