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1,864
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
"... We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks an ..."
Abstract
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Cited by 203 (22 self)
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We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks
J.C.: Best practices for convolutional neural networks applied to visual document analysis
- In: Int’l Conference on Document Analysis and Recognition
, 2003
"... Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a confusing plethora of different neural network methods that are used in the literature and in industry. This paper describes a set of concrete best practices that document analys ..."
Abstract
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Cited by 201 (7 self)
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analysis researchers can use to get good results with neural networks. The most important practice is getting a training set as large as possible: we expand the training set by adding a new form of distorted data. The next most important practice is that convolutional neural networks are better suited
Learning methods for generic object recognition with invariance to pose and lighting
- In Proceedings of CVPR’04
, 2004
"... We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter. A large dataset comprising stereo image pairs of 50 uniform-colored toys under 36 angles, 9 azimuths, and 6 lighting co ..."
Abstract
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Cited by 253 (18 self)
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of the objects with various amounts of variability and surrounding clutter were used for training and testing. Nearest Neighbor methods, Support Vector Machines, and Convolutional Networks, operating on raw pixels or on PCA-derived features were tested. Test error rates for unseen object instances placed
monoliths
"... Many applications proposed for graphene require multiple sheets be assembled into a monolithic structure. The ability to maintain structural integrity upon large deformation is essential to ensure a macroscopic material which functions reliably. However, it has remained a great challenge to achieve ..."
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high elasticity in three-dimensional graphene networks. Here we report that the marriage of graphene chemistry with ice physics can lead to the formation of ultralight and superelastic graphene-based cellular monoliths. Mimicking the hierarchical structure of natural cork, the resulting materials can
Overfeat: Integrated recognition, localization and detection using convolutional networks
- http://arxiv.org/abs/1312.6229
"... ar ..."
Rich feature hierarchies for accurate object detection and semantic segmentation
"... Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex en-semble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scala ..."
Abstract
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Cited by 251 (23 self)
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and scalable detection algorithm that im-proves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012—achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural net-works (CNNs) to bottom-up region proposals
Convolutional Networks for Images, Speech, and Time-Series
, 1995
"... INTRODUCTION The ability of multilayer back-propagation networks to learn complex, high-dimensional, nonlinear mappings from large collections of examples makes them obvious candidates for image recognition or speech recognition tasks (see PATTERN RECOGNITION AND NEURAL NETWORKS). In the traditional ..."
Abstract
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Cited by 134 (5 self)
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INTRODUCTION The ability of multilayer back-propagation networks to learn complex, high-dimensional, nonlinear mappings from large collections of examples makes them obvious candidates for image recognition or speech recognition tasks (see PATTERN RECOGNITION AND NEURAL NETWORKS
or convolution?
, 2003
"... The remarkable generation of scores of increasingly sophisticated mouse models of mammary cancer over the past two decades has provided tremendous insights into molecular derangements that can lead to cancer. The relationships of these models to human breast cancer, however, remain problematic. Rece ..."
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. Genomic approaches to cancer are generating huge datasets that represent a complex system of underlying networks
Design of Logical Topologies for Wavelength-Routed Optical Networks
- IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
, 1996
"... This paper studies the problem of designing a logical topology over a wavelengthrouted all-optical network physical topology. The physical topology consists of the nodes and fiber links in the network. On an all-optical network physical topology, we can set up lightpaths between pairs of nodes, w ..."
Abstract
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Cited by 204 (4 self)
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This paper studies the problem of designing a logical topology over a wavelengthrouted all-optical network physical topology. The physical topology consists of the nodes and fiber links in the network. On an all-optical network physical topology, we can set up lightpaths between pairs of nodes
Results 11 - 20
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1,864
