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 ArticleDOI
SinGAN: Learning a Generative Model From a Single Natural Image
TL;DR: SinGAN, an unconditional generative model that can be learned from a single natural image, is introduced, trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image.
Journal ArticleDOI
Machine Learning: Algorithms, Real-World Applications and Research Directions
TL;DR: In this paper, the authors present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application and highlight the challenges and potential research directions based on their study.
Proceedings ArticleDOI
NISP: Pruning Networks Using Neuron Importance Score Propagation
Ruichi Yu,Ang Li,Chun-Fu Chen,Jui-Hsin Lai,Vlad I. Morariu,Xintong Han,Mingfei Gao,Ching-Yung Lin,Larry S. Davis +8 more
TL;DR: Zhang et al. as mentioned in this paper proposed the Neuron Importance Score Propagation (NISP) algorithm to propagate the importance scores of final responses to every neuron in the network.
Journal ArticleDOI
A deep learning framework for financial time series using stacked autoencoders and long-short term memory
TL;DR: A novel deep learning framework where wavelet transforms, stacked autoencoders and long-short term memory are combined for stock price forecasting and shows that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.
Posted Content
VideoBERT: A Joint Model for Video and Language Representation Learning.
TL;DR: In this article, a joint visual-linguistic model is proposed to learn high-level features without any explicit supervision, inspired by its recent success in language modeling, and it outperforms the state-of-the-art on video captioning, and quantitative results verify that the model learns highlevel semantic features.
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.