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A Deep Learning Pipeline for Image U...

Sermanet, Pierre.

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A Deep Learning Pipeline for Image Understanding and Acoustic Modeling.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Deep Learning Pipeline for Image Understanding and Acoustic Modeling./
作者:	Sermanet, Pierre.
面頁冊數:	96 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
Contained By:	Dissertation Abstracts International75-07B(E).
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3614897
ISBN:	9781303805936

A Deep Learning Pipeline for Image Understanding and Acoustic Modeling.
Sermanet, Pierre.

A Deep Learning Pipeline for Image Understanding and Acoustic Modeling. - 96 p.

Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.

Thesis (Ph.D.)--New York University, 2014.

One of the biggest challenges artificial intelligence faces is making sense of the real world through sensory signals such as audio or video. Noisy inputs, varying object viewpoints, deformations and lighting conditions turn it into a high-dimensional problem which cannot be efficiently solved without learning from data. This thesis explores a general way of learning from high dimensional data (video, images, audio, text, financial data, etc.) called deep learning. It strives on the increasingly large amounts of data available to learn robust and invariant internal features in a hierarchical manner directly from the raw signals. We propose an unified pipeline for feature learning, recognition, localization and detection using Convolutional Networks (ConvNets) that can obtain state-of-the-art accuracy on a number of pattern recognition tasks, including acoustic modeling for speech recognition and object recognition in computer vision. ConvNets are particularly well suited for learning from continuous signals in terms of both accuracy and efficiency. Additionally, a novel and general deep learning approach to detection is proposed and successfully demonstrated on the most challenging vision datasets. We then generalize it to other modalities such as speech data. This approach allows accurate localization and detection objects in images or phones in voice signals by learning to predict boundaries from internal representations. We extend the reach of deep learning from classification to detection tasks in an integrated fashion by learning multiple tasks using a single deep model. This work is among the first to outperform human vision and establishes a new state of the art on some computer vision and speech recognition benchmarks.

ISBN: 9781303805936Subjects--Topical Terms:

626642
Computer Science.

A Deep Learning Pipeline for Image Understanding and Acoustic Modeling.
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