ImageNet classification with deep convolutional neural networks

Krizhevsky, Alex, Sutskever, Ilya, Hinton, Geoffrey E.·2017·Communications of the ACM

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  2. A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance
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  3. Non-local Deep Features for Salient Object Detection
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  28. Convolutional Neural Networks for Inverse Problems in Imaging: A Review
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  67. Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey
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  68. DSOD: Learning Deeply Supervised Object Detectors from Scratch
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  69. Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
    Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox · 2014 · arXiv (Cornell University)
  70. Heterogeneous Network Embedding via Deep Architectures
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  71. Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines
    He, Lin, Li, Jun, Liu, Chenying, Li, Shutao · 2018 · IEEE Transactions on Geoscience and Remote Sensing
  72. Fruits and vegetables quality evaluation using computer vision: A review
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  73. Spiking Neural Networks and Their Applications: A Review
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  74. Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions
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  75. Deep learning for plant identification using vein morphological patterns
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  77. HIN2Vec
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  78. Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation
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  79. 3D deeply supervised network for automated segmentation of volumetric medical images
    Qi Dou, Lequan Yu, Hao Chen, Yueming Jin, Xin Yang, Jing Qin, Pheng‐Ann Heng · 2017 · Medical Image Analysis
  80. AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
    Albarqouni, Shadi, Baur, Christoph, Achilles, Felix, Belagiannis, Vasileios, Demirci, Stefanie, Navab, Nassir · 2016 · IEEE Transactions on Medical Imaging
  81. Deep Learning and Its Applications in Biomedicine
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  82. A deep learning approach for Parkinson’s disease diagnosis from EEG signals
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  83. Evaluation of Output Embeddings for Fine-grained Image Classification
    Zeynep Akata, Reed, S., Walter, D., Honglak Lee, Bernt Schiele · 2015 · Max Planck Digital Library
  84. A Review of Machine Learning and IoT in Smart Transportation
    Zantalis, Fotios, Koulouras, Grigorios, Karabetsos, Sotiris, Kandris, Dionisis · 2019 · Future Internet
  85. Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
    Emre Çakır, Giambattista Parascandolo, Toni Heittola, Heikki J. Huttunen, Tuomas I. Virtanen · 2017 · IEEE/ACM Transactions on Audio Speech and Language Processing
  86. SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging
    Huy Phan, Fernando Andreotti, Navin Cooray, Oliver Y. Chén, Maarten De Vos · 2019 · IEEE Transactions on Neural Systems and Rehabilitation Engineering
  87. Adaptive batch normalization for practical domain adaptation
    Li, Yanghao, Wang, Naiyan, Shi, Jianping, Hou, Xiaodi, Liu, Jiaying · 2018 · Pattern Recognition
  88. A comprehensive review of deep learning applications in hydrology and water resources
    Muhammed Ali Sit, Bekir Zahit Demiray, Zhongrun Xiang, Gregory J. Ewing, Yusuf Sermet, İbrahim Demir · 2020 · Water Science & Technology
  89. Untitled work
    Giger · 2018 · 10.1016/j.jacr.2017.12.028
  90. CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows
    Gudovskiy, Denis, Ishizaka, Shun, Kozuka, Kazuki · 2022 · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  91. MatConvNet - Convolutional Neural Networks for MATLAB
    Andrea Vedaldi, Karel Lenc · 2014 · arXiv (Cornell University)
  92. L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space
    Yurun Tian, Bin Fan, Fuchao Wu · 2017 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  93. Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
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  94. Deep Learning Methods for Reynolds-Averaged Navier–Stokes Simulations of Airfoil Flows
    Nils Thuerey, Konstantin Weißenow, Lukas Prantl, Xiangyu Hu · 2019 · AIAA Journal
  95. Untitled work
    2020 · 10.1109/icra40945.2020.9196885
  96. MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes
    Ha, Qishen, Watanabe, Kohei, Karasawa, Takumi, Ushiku, Yoshitaka, Harada, Tatsuya · 2017 · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  97. Deep Stereo: Learning to Predict New Views from the World's Imagery
    John Flynn, Ivan Neulander, James F. Philbin, Noah Snavely · 2016 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  98. Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks
    Michael Kampffmeyer, Arnt-Børre Salberg, Robert Jenssen · 2016 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  99. Learning Video Object Segmentation from Static Images
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  100. Repulsion Loss: Detecting Pedestrians in a Crowd
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