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
More filters
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
More filters
Posted Content
Holistically-Nested Edge Detection
Saining Xie,Zhuowen Tu +1 more
TL;DR: HED performs image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets, and automatically learns rich hierarchical representations that are important in order to resolve the challenging ambiguity in edge and object boundary detection.
Journal ArticleDOI
Original approach for the localisation of objects in images
R. Vaillant,C. Monrocq,Y. Le Cun +2 more
TL;DR: An original approach is presented for the localisation of objects in an image which approach is neuronal and has two steps and is applied to the problem of localising faces in images.
Journal ArticleDOI
Sparse coding based visual tracking: Review and experimental comparison
TL;DR: This paper first analyzes the benefits of using sparse coding in visual tracking and then categorizes these methods into appearance modeling based on sparse coding (AMSC) and target searchingbased on sparse representation (TSSR) as well as their combination.
Proceedings ArticleDOI
Combining randomization and discrimination for fine-grained image categorization
TL;DR: Results show that the proposed random forest with discriminative decision trees algorithm identifies semantically meaningful visual information and outperforms state-of-the-art algorithms on various datasets.
Posted Content
Dynamic Network Surgery for Efficient DNNs
Yiwen Guo,Anbang Yao,Yurong Chen +2 more
TL;DR: In this article, the authors proposed a dynamic network surgery, which can remarkably reduce the network complexity by making on-the-fly connection pruning and properly incorporate connection splicing into the whole process to avoid incorrect pruning.