Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
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TLDR
Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).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 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.read more
Citations
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Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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Deep Residual Learning for Image Recognition
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
References
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Proceedings ArticleDOI
Discriminative figure-centric models for joint action localization and recognition
Tian Lan,Yang Wang,Greg Mori +2 more
TL;DR: This paper develops an algorithm for action recognition and localization in videos that does not require reliable human detection and tracking as input and uses a figure-centric visual word representation.
Proceedings ArticleDOI
Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction
TL;DR: This paper proposes two pose-normalized descriptors based on computationally-efficient deformable part models based on strongly-supervised DPM parts, which enable pooling across pose and viewpoint, in turn facilitating tasks such as fine-grained recognition and attribute prediction.
Journal ArticleDOI
Large-Margin Multi-ViewInformation Bottleneck
Chang Xu,Dacheng Tao,Chao Xu +2 more
TL;DR: This paper forms the problem as one of encoding a communication system with multiple senders, each of which represents one view of the data, and derives the robustness and generalization error bound of the proposed algorithm, and reveals the specific properties of multi-view learning.
Journal ArticleDOI
Structure from motion using line correspondences
TL;DR: A theory is presented that describes a closed form solution to the motion and structure determination problem from line correspondences in three views, compared with previous ones that are based on nonlinear equations and iterative methods.
Proceedings ArticleDOI
Smooth Representation Clustering
TL;DR: It is found that grouping effect is important for subspace clustering, which should be explicitly enforced in the data self-representation model, rather than implicitly implied by the model as in some prior work.