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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Proceedings Article
Genetic CNN
Lingxi Xie,Alan L. Yuille +1 more
TL;DR: Zhang et al. as mentioned in this paper proposed an encoding method to represent each network structure in a fixed-length binary string, which is initialized by generating a set of randomized individuals and defined standard genetic operations, e.g., selection, mutation and crossover, to generate competitive individuals and eliminate weak ones.
Proceedings ArticleDOI
Deep Stereo: Learning to Predict New Views from the World's Imagery
TL;DR: This work presents a novel deep architecture that performs new view synthesis directly from pixels, trained from a large number of posed image sets, and is the first to apply deep learning to the problem ofnew view synthesis from sets of real-world, natural imagery.
Posted Content
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.
Md. Zahangir Alom,Tarek M. Taha,Christopher Yakopcic,Stefan Westberg,Paheding Sidike,Mst Shamima Nasrin,Brian Van Essen,Abdul A. S. Awwal,Vijayan K. Asari +8 more
TL;DR: This report presents a brief survey on development of DL approaches, including Deep Neural Network (DNN), Convolutional neural network (CNN), Recurrent Neural network (RNN) including Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).
Journal ArticleDOI
Video Captioning With Attention-Based LSTM and Semantic Consistency
TL;DR: A novel end-to-end framework named aLSTMs, an attention-based LSTM model with semantic consistency, to transfer videos to natural sentences with competitive or even better results than the state-of-the-art baselines for video captioning in both BLEU and METEOR.
Proceedings ArticleDOI
Boosting Image Captioning with Attributes
TL;DR: Li et al. as discussed by the authors proposed a Long Short-Term Memory with Attributes (LSTM-A) architecture that integrates attributes into the successful Convolutional Neural Networks (CNNs) plus RNNs (RNNs) image captioning framework, by training them in an end-to-end manner.
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.