Open AccessProceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
- Vol. 1, pp 448-456
TLDR
Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.Abstract:
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters.read more
Citations
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A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement.
Ke Tan,DeLiang Wang +1 more
TL;DR: This paper incorporates a convolutional encoderdecoder (CED) and long short-term memory (LSTM) into the CRN architecture, which leads to a causal system that is naturally suitable for real-time processing.
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A review on deep convolutional neural networks
Neena Aloysius,M. Geetha +1 more
TL;DR: This work has done a thorough literature survey of Convolutional Neural Networks which is the widely used framework of deep learning and has reviewed all the variations emerged over time to suit various applications and a small discussion on the available frameworks for the implementation of the same.
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Fully Deep Blind Image Quality Predictor
Jongyoo Kim,Sanghoon Lee +1 more
TL;DR: A blind image evaluator based on a convolutional neural network (BIECON) is proposed that follows the FR-IQA behavior using the local quality maps as intermediate targets for conventional neural networks, which leads to NR- IQA prediction accuracy that is comparable with that of state-of-the-art FR-iqA methods.
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Context-Aware Synthesis for Video Frame Interpolation
Simon Niklaus,Feng Liu +1 more
TL;DR: A context-aware synthesis approach that warps not only the input frames but also their pixel-wise contextual information and uses them to interpolate a high-quality intermediate frame and outperforms representative state-of-the-art approaches.
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Spatio-Temporal AutoEncoder for Video Anomaly Detection
TL;DR: A novel model called Spatio-Temporal AutoEncoding (ST AutoEncoder or STAE), which utilizes deep neural networks to learn video representation automatically and extracts features from both spatial and temporal dimensions by performing 3-dimensional convolutions, which enhances the motion feature learning in videos.
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Journal Article
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Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Proceedings Article
Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair,Geoffrey E. Hinton +1 more
TL;DR: Restricted Boltzmann machines were developed using binary stochastic hidden units that learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset.