Caffe: Convolutional architecture for fast feature embedding
Abstract Caffe provides multimedia scientists and practitioners with a clean and modifiable
framework for state-of-the-art deep learning algorithms and a collection of reference models.
The framework is a BSD-licensed C++ library with Python and MATLAB bindings for ...
framework for state-of-the-art deep learning algorithms and a collection of reference models.
The framework is a BSD-licensed C++ library with Python and MATLAB bindings for ...
Cited by 2569 Related articles All 11 versions Cite SaveSaving...Error saving. Try again? More EBSCOhost Full Text Fewer
Rich feature hierarchies for accurate object detection and semantic segmentation
Abstract Object detection performance, as measured on the canonical PASCAL VOC
dataset, has plateaued in the last few years. The best-performing methods are complex
ensemble systems that typically combine multiple low-level image features with high-level ...
dataset, has plateaued in the last few years. The best-performing methods are complex
ensemble systems that typically combine multiple low-level image features with high-level ...
Cited by 1903 Related articles All 24 versions Cite SaveSaving...Error saving. Try again? More Fewer
Very deep convolutional networks for large-scale image recognition
K Simonyan, A Zisserman - arXiv preprint arXiv:1409.1556, 2014 - arxiv.org
Abstract: In this work we investigate the effect of the convolutional network depth on its
accuracy in the large-scale image recognition setting. Our main contribution is a thorough
evaluation of networks of increasing depth using an architecture with very small (3x3) ...
accuracy in the large-scale image recognition setting. Our main contribution is a thorough
evaluation of networks of increasing depth using an architecture with very small (3x3) ...
Representation learning: A review and new perspectives
Abstract—The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different representations can
entangle and hide more or less the different explanatory factors of variation behind the ...
representation, and we hypothesize that this is because different representations can
entangle and hide more or less the different explanatory factors of variation behind the ...
Cited by 1357 Related articles All 41 versions Cite SaveSaving...Error saving. Try again? More EBSCOhost Full Text Fewer
Going deeper with convolutions
Abstract We propose a deep convolutional neural network architecture codenamed
Inception that achieves the new state of the art for classification and detection in the
ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC2014). The main ...
Inception that achieves the new state of the art for classification and detection in the
ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC2014). The main ...
Cited by 1688 Related articles All 19 versions Cite SaveSaving...Error saving. Try again? More Fewer
Visualizing and understanding convolutional networks
Abstract Large Convolutional Network models have recently demonstrated impressive
classification performance on the ImageNet benchmark Krizhevsky et al.[18]. However there
is no clear understanding of why they perform so well, or how they might be improved. In ...
classification performance on the ImageNet benchmark Krizhevsky et al.[18]. However there
is no clear understanding of why they perform so well, or how they might be improved. In ...
Cited by 1194 Related articles All 17 versions Cite SaveSaving...Error saving. Try again? More EBSCOhost Full Text Fewer
Imagenet large scale visual recognition challenge
Abstract The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object
category classification and detection on hundreds of object categories and millions of
images. The challenge has been run annually from 2010 to present, attracting ...
category classification and detection on hundreds of object categories and millions of
images. The challenge has been run annually from 2010 to present, attracting ...
Cited by 1316 Related articles All 10 versions Cite SaveSaving...Error saving. Try again? More EBSCOhost Full Text Fewer
[PDF][PDF] Dropout: a simple way to prevent neural networks from overfitting.
Abstract Deep neural nets with a large number of parameters are very powerful machine
learning systems. However, overfitting is a serious problem in such networks. Large
networks are also slow to use, making it difficult to deal with overfitting by combining the ...
learning systems. However, overfitting is a serious problem in such networks. Large
networks are also slow to use, making it difficult to deal with overfitting by combining the ...
Cited by 1330 Related articles All 16 versions Cite SaveSaving...Error saving. Try again? More EBSCOhost Full Text View as HTML Fewer
Overfeat: Integrated recognition, localization and detection using convolutional networks
Abstract: We present an integrated framework for using Convolutional Networks for
classification, localization and detection. We show how a multiscale and sliding window
approach can be efficiently implemented within a ConvNet. We also introduce a novel ...
classification, localization and detection. We show how a multiscale and sliding window
approach can be efficiently implemented within a ConvNet. We also introduce a novel ...
[PDF][PDF] DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition.
Abstract 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 ...
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 ...
Cited by 923 Related articles All 14 versions Cite SaveSaving...Error saving. Try again? More View as HTML Fewer