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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Posted Content
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
TL;DR: Zhang et al. as discussed by the authors proposed an energy-aware pruning algorithm for CNNs that directly uses energy consumption estimation of a CNN to guide the pruning process, and the energy estimation methodology uses parameters extrapolated from actual hardware measurements that target realistic battery-powered system setups.
Proceedings ArticleDOI
Deep LAC: Deep localization, alignment and classification for fine-grained recognition
TL;DR: A valve linkage function (VLF) for back-propagation chaining is proposed to form the deep localization, alignment and classification (LAC) system and can adaptively compromise the errors of classification and alignment when training the LAC model.
Book ChapterDOI
Mixed Pooling for Convolutional Neural Networks
TL;DR: A novel feature pooling method is proposed to regularize CNNs, which replaces the deterministic pooling operations with a stochastic procedure by randomly using the conventional max pooling and average pooling methods.
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Data-free parameter pruning for Deep Neural Networks
Suraj Srinivas,R. Venkatesh Babu +1 more
TL;DR: It is shown how similar neurons are redundant, and a systematic way to remove them is proposed, which can be applied on top of most networks with a fully connected layer to give a smaller network.
Proceedings ArticleDOI
Birdsnap: Large-Scale Fine-Grained Visual Categorization of Birds
Thomas Berg,Jiongxin Liu,Seung W han Lee,Michelle L. Alexander,David W. Jacobs,Peter N. Belhumeur +5 more
TL;DR: The problem of large-scale fine-grained visual categorization is addressed, describing new methods used to produce an online field guide to 500 North American bird species and state-of-the-art recognition performance is shown on a new, large dataset made publicly available.