Journal ArticleDOI
Convolutional neural network learning for generic data classification
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TLDR
This paper proposes to enable CNN for learning from generic data to improve classification accuracy, and proposes to convert each instance of the original dataset into a synthetic matrix/image format.About:
This article is published in Information Sciences.The article was published on 2019-03-01. It has received 29 citations till now. The article focuses on the topics: Feature learning & Deep learning.read more
Citations
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Journal ArticleDOI
Survey on categorical data for neural networks
TL;DR: This study provides a starting point for research in determining which techniques for preparing qualitative data for use with neural networks are best, and is the first in-depth look at techniques for working with categorical data in neural networks.
Journal ArticleDOI
A survey and taxonomy of adversarial neural networks for text‐to‐image synthesis
TL;DR: The survey first introduces image synthesis and its challenges, and then reviews key concepts such as generative adversarial networks (GANs) and deep convolutional encoder‐decoder neural networks (DCNNs), and proposes a taxonomy to summarize GAN‐based text‐to‐image synthesis into four major categories.
Journal ArticleDOI
A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection
TL;DR: Wang et al. as mentioned in this paper proposed a novel Hierarchical CNN and Gated Recurrent Unit (GRU) framework to model both spatial and temporal relations, termed as HCG, for structural damage detection.
Journal ArticleDOI
Image forgery detection using error level analysis and deep learning
TL;DR: Deep Learning is applied to recognize images of manipulations through the dataset of a fake image and original images via Error Level Analysis on each image and supporting parameters for error rate analysis to find out the compression ratio between the original image and the fake image.
Posted Content
A Hierarchical Deep Convolutional Neural Network and Gated Recurrent Unit Framework for Structural Damage Detection
TL;DR: This work proposes a novel Hierarchical CNN and Gated recurrent unit (GRU) framework to model both spatial and temporal relations, termed as HCG, for structural damage detection, where CNN is utilized to model the spatial relations and the short-term temporal dependencies among sensors, while the output features of CNN are fed into the GRU to learn the long- term temporal dependencies jointly.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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