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
A novel deep learning based framework for the detection and classification of breast cancer using transfer learning
Sana Ullah Khan,Naveed Islam,Zahoor Jan,Ikram Ud Din,Joel J. P. C. Rodrigues,Joel J. P. C. Rodrigues +5 more
TL;DR: A novel deep learning framework for the detection and classification of breast cancer in breast cytology images using the concept of transfer learning is proposed and it has been observed that the proposed framework outclass all the other deep learning architectures in terms of accuracy in detection and classified of breast tumor in cytological images.
Proceedings ArticleDOI
Ask Your Neurons: A Neural-Based Approach to Answering Questions about Images
TL;DR: This work addresses a question answering task on real-world images that is set up as a Visual Turing Test by combining latest advances in image representation and natural language processing and proposes Neural-Image-QA, an end-to-end formulation to this problem for which all parts are trained jointly.
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Variational dropout sparsifies deep neural networks
TL;DR: Variational Dropout is extended to the case when dropout rates are unbounded, a way to reduce the variance of the gradient estimator is proposed and first experimental results with individual drop out rates per weight are reported.
Journal ArticleDOI
Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.
David Van Valen,Takamasa Kudo,Keara Michelle Lane,Derek N. Macklin,Nicolas Quach,Mialy M. DeFelice,Inbal Maayan,Yu Tanouchi,Euan A. Ashley,Markus W. Covert +9 more
TL;DR: Deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.
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Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
TL;DR: In this article, the authors provide a comprehensive survey of state-of-the-art remote sensing deep learning research for remote sensing applications, focusing on theories, tools, and challenges for the remote sensing community.
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.