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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Journal ArticleDOI
T-CNN: Tubelets With Convolutional Neural Networks for Object Detection From Videos
Kai Kang,Hongsheng Li,Junjie Yan,Zeng Xingyu,Bin Yang,Tong Xiao,Cong Zhang,Zhe Wang,Ruohui Wang,Xiaogang Wang,Wanli Ouyang +10 more
TL;DR: A deep learning framework that incorporates temporal and contextual information from tubelets obtained in videos, which dramatically improves the baseline performance of existing still-image detection frameworks when they are applied to videos is proposed, called T-CNN.
Journal ArticleDOI
Convolutional neural networks for hyperspectral image classification
Shiqi Yu,Sen Jia,Chunyan Xu +2 more
TL;DR: An efficient CNN architecture has been proposed to boost its discriminative capability for hyperspectral image classification, in which the original data is used as the input and the final CNN outputs are the predicted class-related results.
Posted Content
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
TL;DR: This review introduces the history of CNN, some classic and advanced CNN models are introduced, and an overview of various convolutions is provided, including those key points making them reach state-of-the-art results.
Journal ArticleDOI
In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images
Eric Christiansen,Samuel Yang,D. Michael Ando,Ashkan Javaherian,Gaia Skibinski,Scott Lipnick,Elliot Mount,Alison O’Neil,Kevan Shah,Alicia K. Lee,Piyush Goyal,William Fedus,William Fedus,Ryan Poplin,Andre Esteva,Andre Esteva,Marc Berndl,Lee L. Rubin,Philip C. Nelson,Steven Finkbeiner,Steven Finkbeiner +20 more
TL;DR: It is shown that a computational machine-learning approach, which is called "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples.
Proceedings ArticleDOI
Stacked Generative Adversarial Networks
TL;DR: SGAN as discussed by the authors proposes a top-down stack of GANs, each of which is trained to generate lower-level representations conditioned on higher-level representation, with a representation discriminator introduced at each feature hierarchy to encourage the representation manifold of the generator to align with that of the bottom-up discriminative network.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.