Recent advances in convolutional neural networks
Jiuxiang Gu,Zhenhua Wang,Jason Kuen,Lianyang Ma,Amir Shahroudy,Bing Shuai,Ting Liu,Xingxing Wang,Gang Wang,Jianfei Cai,Tsuhan Chen +10 more
TLDR
A broad survey of the recent advances in convolutional neural networks can be found in this article, where the authors discuss the improvements of CNN on different aspects, namely, layer design, activation function, loss function, regularization, optimization and fast computation.About:
This article is published in Pattern Recognition.The article was published on 2018-05-01 and is currently open access. It has received 3125 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
Citations
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Journal ArticleDOI
Deep Learning for Generic Object Detection: A Survey
Li Liu,Li Liu,Wanli Ouyang,Xiaogang Wang,Paul Fieguth,Jie Chen,Xinwang Liu,Matti Pietikäinen +7 more
TL;DR: A comprehensive survey of the recent achievements in this field brought about by deep learning techniques, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics.
Journal ArticleDOI
A survey of the recent architectures of deep convolutional neural networks
TL;DR: Deep Convolutional Neural Networks (CNNs) as mentioned in this paper are a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing.
Journal ArticleDOI
Applications of machine learning to machine fault diagnosis: A review and roadmap
TL;DR: A review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective is presented.
Journal ArticleDOI
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi,Jinglan Zhang,Amjad J. Humaidi,Ayad Q. Al-Dujaili,Ye Duan,Omran Al-Shamma,José Santamaría,Mohammed A. Fadhel,Muthana Al-Amidie,Laith Farhan +9 more
TL;DR: In this paper, a comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field is provided, and the challenges and suggested solutions to help researchers understand the existing research gaps.
Journal ArticleDOI
Albumentations: fast and flexible image augmentations
Alexander Buslaev,Vladimir Iglovikov,Eugene Khvedchenya,Alex Parinov,Mikhail Druzhinin,Alexandr A. Kalinin +5 more
TL;DR: Albumentations as mentioned in this paper is a fast and flexible open source library for image augmentation with many various image transform operations available that is also an easy-to-use wrapper around other augmentation libraries.
References
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Improving patch-based scene text script identification with ensembles of conjoined networks
TL;DR: In this paper, a patch-based classification method for script identificattion in the wild is presented. But this method does not address a key characteristic of scene text instances: their extremely variable aspect ratio.
Proceedings ArticleDOI
LCNN: Lookup-Based Convolutional Neural Network
TL;DR: In this article, a lookup-based convolutional neural network (LCNN) is proposed to encode convolutions by few lookups to a dictionary that is trained to cover the space of weights in CNNs.
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An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
Baoguang Shi,Xiang Bai,Cong Yao +2 more
TL;DR: A novel neural network architecture, which integrates feature extraction, sequence modeling and transcription into a unified framework, is proposed, which generates an effective yet much smaller model, which is more practical for real-world application scenarios.
Journal ArticleDOI
Phonemic hidden Markov models with continuous mixture output densities for large vocabulary word recognition
TL;DR: It is shown how phonemic hidden Markov models with Gaussian mixture output densities can be implemented very simply in unimodal transition-based frameworks by allowing multiple transitions from one state to another.
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Diversity Networks
Zelda Mariet,Suvrit Sra +1 more
TL;DR: Divnet offers a more principled, flexible technique for capturing neuronal diversity and thus implicitly enforcing regularization, which enables effective auto-tuning of network architecture and leads to smaller network sizes without hurting performance.