| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Bolei Zhou, David Bau, Aude Oliva, Antonio Torralb. Interpreting Deep Visual Representations via Network Dissection. IEEE transactions on pattern analysis and machine intelligence. vol 41. issue 9. 2019-09-11. PMID:30040625. |
the proposed method quantifies the interpretability of cnn representations by evaluating the alignment between individual hidden units and visual semantic concepts. |
2019-09-11 |
2023-08-13 |
Not clear |
| Peng Tang, Xinggang Wang, Baoguang Shi, Xiang Bai, Wenyu Liu, Zhuowen T. Deep FisherNet for Image Classification. IEEE transactions on neural networks and learning systems. vol 30. issue 7. 2019-09-11. PMID:30403638. |
despite the great success of convolutional neural networks (cnns) for the image classification task on data sets such as cifar and imagenet, cnn's representation power is still somewhat limited in dealing with images that have a large variation in size and clutter, where fisher vector (fv) has shown to be an effective encoding strategy. |
2019-09-11 |
2023-08-13 |
Not clear |
| Siavash Sakhavi, Cuntai Guan, Shuicheng Ya. Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks. IEEE transactions on neural networks and learning systems. vol 29. issue 11. 2019-09-04. PMID:29994075. |
in this paper, we propose a classification framework for mi data by introducing a new temporal representation of the data and also utilizing a convolutional neural network (cnn) architecture for classification. |
2019-09-04 |
2023-08-13 |
human |
| Siavash Sakhavi, Cuntai Guan, Shuicheng Ya. Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks. IEEE transactions on neural networks and learning systems. vol 29. issue 11. 2019-09-04. PMID:29994075. |
the new representation is generated from modifying the filter-bank common spatial patterns method, and the cnn is designed and optimized accordingly for the representation. |
2019-09-04 |
2023-08-13 |
human |
| Yu Zhao, Qinglin Dong, Shu Zhang, Wei Zhang, Hanbo Chen, Xi Jiang, Lei Guo, Xintao Hu, Junwei Han, Tianming Li. Automatic Recognition of fMRI-Derived Functional Networks Using 3-D Convolutional Neural Networks. IEEE transactions on bio-medical engineering. vol 65. issue 9. 2019-06-20. PMID:28641239. |
in recognition of the fact that convolutional neural networks (cnn) has superior capability of representing spatial patterns with huge variability and dealing with large noises, in this paper, we design, apply, and evaluate a deep 3-d cnn framework for automatic, effective, and accurate classification and recognition of large number of functional brain networks reconstructed by sparse representation of whole-brain fmri signals. |
2019-06-20 |
2023-08-13 |
human |
| Wenxi Liu, Yibing Song, Dengsheng Chen, Shengfeng He, Yuanlong Yu, Tao Yan, Gehard P Hancke, Rynson W H La. Deformable Object Tracking With Gated Fusion. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 28. issue 8. 2019-06-17. PMID:30843833. |
the enriched feature representation through deformable convolution facilitates the discrimination of the cnn classifier on the target object and background. |
2019-06-17 |
2023-08-13 |
Not clear |
| Eunhee Kang, Won Chang, Jaejun Yoo, Jong Chul Y. Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network. IEEE transactions on medical imaging. vol 37. issue 6. 2019-05-20. PMID:29870365. |
the new algorithms were inspired by the recent interpretation of the deep cnn as a cascaded convolution framelet signal representation. |
2019-05-20 |
2023-08-13 |
Not clear |
| Qiuling Suo, Fenglong Ma, Ye Yuan, Mengdi Huai, Weida Zhong, Jing Gao, Aidong Zhan. Deep Patient Similarity Learning for Personalized Healthcare. IEEE transactions on nanobioscience. vol 17. issue 3. 2019-05-08. PMID:29994534. |
we use a convolutional neural network (cnn) to capture local important information in ehrs and then feed the learned representation into triplet loss or softmax cross entropy loss. |
2019-05-08 |
2023-08-13 |
Not clear |
| Yi Wang, Zhice Fang, Haoyuan Hon. Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. The Science of the total environment. vol 666. 2019-04-11. PMID:30970504. |
second, different data representation algorithms are developed to construct three novel cnn architectures. |
2019-04-11 |
2023-08-13 |
Not clear |
| Relja Arandjelovic, Petr Gronat, Akihiko Torii, Tomas Pajdla, Josef Sivi. NetVLAD: CNN Architecture for Weakly Supervised Place Recognition. IEEE transactions on pattern analysis and machine intelligence. vol 40. issue 6. 2019-04-04. PMID:28622667. |
finally, we show that the proposed architecture and training procedure significantly outperform non-learnt image representations and off-the-shelf cnn descriptors on challenging place recognition and image retrieval benchmarks. |
2019-04-04 |
2023-08-13 |
Not clear |
| Liang Zheng, Yi Yang, Qi Tia. SIFT Meets CNN: A Decade Survey of Instance Retrieval. IEEE transactions on pattern analysis and machine intelligence. vol 40. issue 5. 2019-03-19. PMID:29610107. |
recently, image representations based on the convolutional neural network (cnn) have attracted increasing interest in the community and demonstrated impressive performance. |
2019-03-19 |
2023-08-13 |
Not clear |
| Jun Li, Wei Zhu, Jun Wang, Wenfei Li, Sheng Gong, Jian Zhang, Wei Wan. RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks. PLoS computational biology. vol 14. issue 11. 2019-03-14. PMID:30481171. |
via an approach that diverges completely from the conventional statistical potentials, our work explored the power of a 3d convolutional neural network (cnn)-based approach as a quality evaluator for rna 3d structures, which used a 3d grid representation of the structure as input without extracting features manually. |
2019-03-14 |
2023-08-13 |
Not clear |
| Xu-Yao Zhang, Fei Yin, Yan-Ming Zhang, Cheng-Lin Liu, Yoshua Bengi. Drawing and Recognizing Chinese Characters with Recurrent Neural Network. IEEE transactions on pattern analysis and machine intelligence. vol 40. issue 4. 2019-03-04. PMID:28436845. |
to recognize chinese characters, previous methods usually adopt the convolutional neural network (cnn) models which require transforming the online handwriting trajectory into image-like representations. |
2019-03-04 |
2023-08-13 |
Not clear |
| Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Pablo Arbelaez, Luc Van Goo. Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks. IEEE transactions on pattern analysis and machine intelligence. vol 40. issue 4. 2019-03-04. PMID:28475046. |
cob is computationally efficient, because it requires a single cnn forward pass for multi-scale contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets. |
2019-03-04 |
2023-08-13 |
Not clear |
| Junwei Han, Hao Chen, Nian Liu, Chenggang Yan, Xuelong L. CNNs-Based RGB-D Saliency Detection via Cross-View Transfer and Multiview Fusion. IEEE transactions on cybernetics. vol 48. issue 11. 2019-03-04. PMID:29990092. |
the second challenge is addressed by a multiview cnn fusion model through a combination layer connecting the representation layers of rgb view and depth view. |
2019-03-04 |
2023-08-13 |
human |
| K Seeliger, M Fritsche, U Güçlü, S Schoenmakers, J-M Schoffelen, S E Bosch, M A J van Gerve. Convolutional neural network-based encoding and decoding of visual object recognition in space and time. NeuroImage. vol 180. issue Pt A. 2019-02-01. PMID:28723578. |
using functional magnetic resonance imaging, cnn representations of visual stimuli have previously been shown to correspond to processing stages in the ventral and dorsal streams of the visual system. |
2019-02-01 |
2023-08-13 |
human |
| Michelle R Greene, Bruce C Hanse. Shared spatiotemporal category representations in biological and artificial deep neural networks. PLoS computational biology. vol 14. issue 7. 2019-01-14. PMID:30040821. |
the goal of the current study is to assess the extent to which sequential scene category representations built by a cnn map onto those built in the human brain as assessed by high-density, time-resolved event-related potentials (erps). |
2019-01-14 |
2023-08-13 |
human |
| Yuan Luo, Yu Cheng, Özlem Uzuner, Peter Szolovits, Justin Starre. Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes. Journal of the American Medical Informatics Association : JAMIA. vol 25. issue 1. 2019-01-02. PMID:29025149. |
unlike typical cnn models, relations between 2 concepts are identified by simultaneously learning separate representations for text segments in a sentence: preceding, concept1, middle, concept2, and succeeding. |
2019-01-02 |
2023-08-13 |
Not clear |
| Jiaxing Ye, Shunya Ito, Nobuyuki Toyam. Computerized Ultrasonic Imaging Inspection: From Shallow to Deep Learning. Sensors (Basel, Switzerland). vol 18. issue 11. 2018-11-08. PMID:30405086. |
using the dataset, we performed a comprehensive experimental comparison of various computer vision techniques, including both conventional methods using hand-crafted visual features and the most recent convolutional neural networks (cnn) which generate multiple-layer stacking for representation learning. |
2018-11-08 |
2023-08-13 |
human |
| Yang You, Cewu Lu, Weiming Wang, Chi-Keung Tan. Relative CNN-RNN: Learning Relative Atmospheric Visibility From Images. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 28. issue 1. 2018-09-24. PMID:30028702. |
the relative cnn-rnn coarse-to-fine model, where cnn stands for convolutional neural network and rnn stands for recurrent neural network, exploits the joint power of relative support vector machine, which has a good ranking representation, and the data-driven deep learning features derived from our novel cnn-rnn model. |
2018-09-24 |
2023-08-13 |
human |