Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

[1]  R. Sarpong,et al.  Bio-inspired synthesis of xishacorenes A, B, and C, and a new congener from fuscol† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c9sc02572c , 2019, Chemical science.

[2]  Lin Yang,et al.  Pairwise based deep ranking hashing for histopathology image classification and retrieval , 2018, Pattern Recognit..

[3]  Qinghua Hu,et al.  Deep super-class learning for long-tail distributed image classification , 2018, Pattern Recognit..

[4]  Bolei Zhou,et al.  Places: A 10 Million Image Database for Scene Recognition , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Yuning Jiang,et al.  MegDet: A Large Mini-Batch Object Detector , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[6]  Bolei Zhou,et al.  Semantic Understanding of Scenes Through the ADE20K Dataset , 2016, International Journal of Computer Vision.

[7]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Guosheng Lin,et al.  Exploring Context with Deep Structured Models for Semantic Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Jiri Matas,et al.  Systematic evaluation of convolution neural network advances on the Imagenet , 2017, Comput. Vis. Image Underst..

[10]  Bolei Zhou,et al.  Scene Parsing through ADE20K Dataset , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Xiang Bai,et al.  Text/non-text image classification in the wild with convolutional neural networks , 2017, Pattern Recognit..

[12]  Xiaogang Wang,et al.  Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Gregory Shakhnarovich,et al.  FractalNet: Ultra-Deep Neural Networks without Residuals , 2016, ICLR.

[14]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[15]  Lior Wolf,et al.  The Loss Surface of Residual Networks: Ensembles and the Role of Batch Normalization , 2016, ArXiv.

[16]  Bolei Zhou,et al.  Places: An Image Database for Deep Scene Understanding , 2016, ArXiv.

[17]  Wenjun Zeng,et al.  Deeply-Fused Nets , 2016, ArXiv.

[18]  Jianxin Wu,et al.  Dense CNN Learning with Equivalent Mappings , 2016, ArXiv.

[19]  Nikos Komodakis,et al.  Wide Residual Networks , 2016, BMVC.

[20]  Anton van den Hengel,et al.  Bridging Category-level and Instance-level Semantic Image Segmentation , 2016, ArXiv.

[21]  Serge J. Belongie,et al.  Residual Networks Behave Like Ensembles of Relatively Shallow Networks , 2016, NIPS.

[22]  Charless C. Fowlkes,et al.  Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation , 2016, ECCV.

[23]  Sebastian Ramos,et al.  The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[25]  Jian Sun,et al.  Instance-Aware Semantic Segmentation via Multi-task Network Cascades , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[28]  Zheng Zhang,et al.  MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems , 2015, ArXiv.

[29]  Xiaoxiao Li,et al.  Semantic Image Segmentation via Deep Parsing Network , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[30]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Seunghoon Hong,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[32]  Jian Sun,et al.  BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[33]  Vibhav Vineet,et al.  Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[34]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[35]  Iasonas Kokkinos,et al.  Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.

[36]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[38]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[39]  Sanja Fidler,et al.  The Role of Context for Object Detection and Semantic Segmentation in the Wild , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[40]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[41]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[42]  Luc Van Gool,et al.  The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.

[43]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[44]  Subhransu Maji,et al.  Semantic contours from inverse detectors , 2011, 2011 International Conference on Computer Vision.

[45]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[46]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

[47]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.