Auswahl der wissenschaftlichen Literatur zum Thema „Dilated convolution“
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Zeitschriftenartikel zum Thema "Dilated convolution"
Wang, Wei, Yiyang Hu, Ting Zou, Hongmei Liu, Jin Wang und Xin Wang. „A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers“. Computational Intelligence and Neuroscience 2020 (01.08.2020): 1–10. http://dx.doi.org/10.1155/2020/8817849.
Der volle Inhalt der QuellePeng, Wenli, Shenglai Zhen, Xin Chen, Qianjing Xiong und Benli Yu. „Study on convolutional recurrent neural networks for speech enhancement in fiber-optic microphones“. Journal of Physics: Conference Series 2246, Nr. 1 (01.04.2022): 012084. http://dx.doi.org/10.1088/1742-6596/2246/1/012084.
Der volle Inhalt der QuelleZhao, Feng, Junjie Zhang, Zhe Meng und Hanqiang Liu. „Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification“. Remote Sensing 13, Nr. 17 (26.08.2021): 3396. http://dx.doi.org/10.3390/rs13173396.
Der volle Inhalt der QuelleChim, Seyha, Jin-Gu Lee und Ho-Hyun Park. „Dilated Skip Convolution for Facial Landmark Detection“. Sensors 19, Nr. 24 (04.12.2019): 5350. http://dx.doi.org/10.3390/s19245350.
Der volle Inhalt der QuelleSong, Zhendong, Yupeng Ma, Fang Tan und Xiaoyi Feng. „Hybrid Dilated and Recursive Recurrent Convolution Network for Time-Domain Speech Enhancement“. Applied Sciences 12, Nr. 7 (29.03.2022): 3461. http://dx.doi.org/10.3390/app12073461.
Der volle Inhalt der QuelleTang, Jingfan, Meijia Zhou, Pengfei Li, Min Zhang und Ming Jiang. „Crowd Counting Based on Multiresolution Density Map and Parallel Dilated Convolution“. Scientific Programming 2021 (20.01.2021): 1–10. http://dx.doi.org/10.1155/2021/8831458.
Der volle Inhalt der QuelleZhang, Jianming, Chaoquan Lu, Jin Wang, Lei Wang und Xiao-Guang Yue. „Concrete Cracks Detection Based on FCN with Dilated Convolution“. Applied Sciences 9, Nr. 13 (01.07.2019): 2686. http://dx.doi.org/10.3390/app9132686.
Der volle Inhalt der QuelleCao, Ruifen, Xi Pei, Ning Ge und Chunhou Zheng. „Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning“. Technology in Cancer Research & Treatment 20 (01.01.2021): 153303382110342. http://dx.doi.org/10.1177/15330338211034284.
Der volle Inhalt der QuelleWang, Ran, Ruyu Shi, Xiong Hu und Changqing Shen. „Remaining Useful Life Prediction of Rolling Bearings Based on Multiscale Convolutional Neural Network with Integrated Dilated Convolution Blocks“. Shock and Vibration 2021 (25.01.2021): 1–11. http://dx.doi.org/10.1155/2021/6616861.
Der volle Inhalt der QuelleMadych, W. R. „Limits of Dilated Convolution Transforms“. SIAM Journal on Mathematical Analysis 16, Nr. 3 (Mai 1985): 551–58. http://dx.doi.org/10.1137/0516041.
Der volle Inhalt der QuelleDissertationen zum Thema "Dilated convolution"
Khalfaoui, Hassani Ismail. „Convolution dilatée avec espacements apprenables“. Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES017.
Der volle Inhalt der QuelleIn this thesis, we develop and study the Dilated Convolution with Learnable Spacings (DCLS) method. The DCLS method can be considered as an extension of the standard dilated convolution method, but in which the positions of the weights of a neural network are learned during training by the gradient backpropagation algorithm, thanks to an interpolation technique. We empirically demonstrate the effectiveness of the DCLS method by providing concrete evidence from numerous supervised learning experiments. These experiments are drawn from the fields of computer vision, audio, and speech processing, and all show that the DCLS method has a competitive advantage over standard convolution techniques, as well as over several advanced convolution methods. Our approach is structured in several steps, starting with an analysis of the literature and existing convolution techniques that preceded the development of the DCLS method. We were particularly interested in the methods that are closely related to our own and that remain essential to capture the nuances and uniqueness of our approach. The cornerstone of our study is the introduction and application of the DCLS method to convolutional neural networks (CNNs), as well as to hybrid architectures that rely on both convolutional and visual attention approaches. The DCLS method is particularly noteworthy for its capabilities in supervised computer vision tasks such as classification, semantic segmentation, and object detection, all of which are essential tasks in the field. Having originally developed the DCLS method with bilinear interpolation, we explored other interpolation methods that could replace the bilinear interpolation conventionally used in DCLS, and which aim to make the position parameters of the weights in the convolution kernel differentiable. Gaussian interpolation proved to be slightly better in terms of performance. Our research then led us to apply the DCLS method in the field of spiking neural networks (SNNs) to enable synaptic delay learning within a neural network that could eventually be transferred to so-called neuromorphic chips. The results show that the DCLS method stands out as a new state-of-the-art technique in SNN audio classification for certain benchmark tasks in this field. These tasks involve datasets with a high temporal component. In addition, we show that DCLS can significantly improve the accuracy of artificial neural networks for the multi-label audio classification task, a key achievement in one of the most important audio classification benchmarks. We conclude with a discussion of the chosen experimental setup, its limitations, the limitations of our method, and our results
Highlander, Tyler Clayton. „Conditional Dilated Attention Tracking Model - C-DATM“. Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1564652134758139.
Der volle Inhalt der QuelleBörjesson, Lukas. „Forecasting Financial Time Series through Causal and Dilated Convolutional Neural Networks“. Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167331.
Der volle Inhalt der QuelleYeh, Pin-Yi, und 葉品儀. „Multi-Scale Neural Network with Dilated Convolutions for Image Deblurring“. Thesis, 2019. http://ndltd.ncl.edu.tw/handle/vgs5cw.
Der volle Inhalt der Quelle國立臺灣科技大學
資訊工程系
107
Several deep learning-based approaches are successful in single image deblurring, particularly, convolutional neural networks (CNN). Unlike traditional methods which try to estimate the blur kernel to extract the latent sharp image, CNN-based methods can directly find the mapping from the blurry input image to the latent sharp image. CNN usually has many layers to represent complex spatial relationships, and down-sampling layers are used to reduce the number of parameters (e.g., encoder-decoder architecture). However, down-sampling causes some spatial information to be lost, and this information could be useful in deblurring large regions. The receptive field is the spatial coverage of each feature, and increasing its value allows less loss of spatial information. We used dilated convolution to increase the receptive field of the features without increasing the number of parameters. Furthermore, the "coarse-to-fine" strategy is applied to the network to the blurry input image at different scales in this thesis. By using this strategy, we can progressively improve the outputs, and allow us to capture details from different scales, without adding more parameters. We show that the proposed model not only has better results with the state-of-the-art but also has faster execution time.
Liu, Chien-Chung, und 劉建忠. „Improved Image Super Resolution Technology Based on Dilated Convolutional Neural Network“. Thesis, 2018. http://ndltd.ncl.edu.tw/handle/w6cn2k.
Der volle Inhalt der Quelle國立臺中科技大學
資訊工程系碩士班
106
Image super resolution is wide application in image processing and computer vision. Because original super resolution image can’t be irreversible and it have distorted pixel values after the image is enlarged are challenging subjects. This paper proposed two architectures which is using convolutional neural network architecture of deep learning to carry out image super resolution. They estimate pixels of super resolution image by neurons of convolutional neural network. The first architecture is reduced dilated convolutional neural network. It reduces dilated convolutional neural network to six convolutional layers. In the second layer to fourth layer use convolution of double dilated rate. The first layer output concatenates the fourth layer output and the second layer output concatenates the third layer output are to deeper learning. The other is wide dilated convolutional neural network. It lets input pass convolutions of difference dilated rate to get output. It achieves wide learning. Neural network learns convolutional input of difference dilated rate by concatenating two outputs to be input of next layer at the same time. It is able to more detail feature extraction and achieve effect of wide learning. This experiments use the parameters of convolutional neural network employed dilated convolutional neural network architecture. The experimental parameters include epoch, validation split, validation mode, sub image size, sub image number, batch size. The experiments appoint appropriate parameters to be 500 epoch, 0.2 validation split, random single sub image which is sub images of the image, 41×41 sub image size, 50 sub image number, 64 batch size. Experimental results appoint PSNR of reduced dilated convolutional network higher than dilated convolutional neural network 0.13dB and strand error smaller 0.07dB. PSNR of wide dilated convolutional network higher than dilated convolutional neural network 0.08dB and strand error smaller 0.09dB. Experiments also include difference scale of image super resolution and using difference types of data sets to test difference on the two proposed architectures. Final, proposed method applied to surveillance system. Results appoint image super resolution is able to enhance part of image features. In noise is improved, image texture isn’t blurry after image super resolution.
Buchteile zum Thema "Dilated convolution"
Zhang, Jinglu, Yinyu Nie, Yao Lyu, Hailin Li, Jian Chang, Xiaosong Yang und Jian Jun Zhang. „Symmetric Dilated Convolution for Surgical Gesture Recognition“. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 409–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59716-0_39.
Der volle Inhalt der QuelleShen, Falong, und Gang Zeng. „Gaussian Dilated Convolution for Semantic Image Segmentation“. In Advances in Multimedia Information Processing – PCM 2018, 324–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00776-8_30.
Der volle Inhalt der QuelleHu, Haigen, Chenghan Yu, Qianwei Zhou, Qiu Guan und Qi Chen. „SAMDConv: Spatially Adaptive Multi-scale Dilated Convolution“. In Pattern Recognition and Computer Vision, 460–72. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8543-2_37.
Der volle Inhalt der QuelleGupta, Sachin, Priya Goyal, Bhuman Vyas, Mohammad Shabaz, Suchitra Bala und Aws Zuhair Sameen. „Dilated convolution model for lightweight neural network“. In Next Generation Computing and Information Systems, 119–26. London: CRC Press, 2024. http://dx.doi.org/10.1201/9781003466383-20.
Der volle Inhalt der QuelleSun, Wei, Xijie Zhou, Xiaorui Zhang und Xiaozheng He. „A Lightweight Neural Network Combining Dilated Convolution and Depthwise Separable Convolution“. In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 210–20. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48513-9_17.
Der volle Inhalt der QuelleQian, Likuan, Yuanfeng Lian, Qian Wei, Shuangyuan Wu und Jianbin Zhang. „ODCN: Optimized Dilated Convolution Network for 3D Shape Segmentation“. In Pattern Recognition and Computer Vision, 378–89. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31726-3_32.
Der volle Inhalt der QuelleWu, Yan, Wei Jiang, Jiqian Li und Tao Yang. „Speeding Up Dilated Convolution Based Pedestrian Detection with Tensor Decomposition“. In Intelligent Computing Methodologies, 117–27. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63315-2_11.
Der volle Inhalt der QuellePan, Xiaoying, Dong Dai, Hongyu Wang, Xingxing Liu und Weidong Bai. „Nasopharyngeal Organ Segmentation Algorithm Based on Dilated Convolution Feature Pyramid“. In Lecture Notes in Electrical Engineering, 45–58. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6963-7_4.
Der volle Inhalt der QuelleWei, Xinlei, Yingji Liu, Wei Zhou, Haiying Xia, Daxin Tian und Ruifen Cheng. „Traffic Crowd Congested Scene Recognition Based on Dilated Convolution Network“. In Communications in Computer and Information Science, 134–46. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1160-5_12.
Der volle Inhalt der QuelleTureckova, Alzbeta, und Antonio J. Rodríguez-Sánchez. „ISLES Challenge: U-Shaped Convolution Neural Network with Dilated Convolution for 3D Stroke Lesion Segmentation“. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 319–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11723-8_32.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Dilated convolution"
Liu, Jen-Yu, und Yi-Hsuan Yang. „Dilated Convolution with Dilated GRU for Music Source Separation“. In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/655.
Der volle Inhalt der QuelleYang, Junyan, und Jie Jiang. „Dilated-CBAM: An Efficient Attention Network with Dilated Convolution“. In 2021 IEEE International Conference on Unmanned Systems (ICUS). IEEE, 2021. http://dx.doi.org/10.1109/icus52573.2021.9641248.
Der volle Inhalt der QuelleHighlander, Tyler, Bernard Abayowa, Mateen Rizki und Hamilton Scott Clouse. „Conditional Dilated Convolution Attention Tracking Model“. In 2019 Third IEEE International Conference on Robotic Computing (IRC). IEEE, 2019. http://dx.doi.org/10.1109/irc.2019.00096.
Der volle Inhalt der QuelleHua, Chen, Kuang Xu und Tong Tong. „Crowd Counting with Dilated Inception Convolution“. In ICCAI '21: 2021 7th International Conference on Computing and Artificial Intelligence. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3467707.3467738.
Der volle Inhalt der QuelleWang, Xian, Lingqiao Liu und Qinfeng Shi. „Enhancing Piano Transcription by Dilated Convolution“. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2020. http://dx.doi.org/10.1109/icmla51294.2020.00224.
Der volle Inhalt der QuelleYin, Zhanhong, Renchao Qin, Chengzhuo Ye, Ya Li, Yaying He, Yue Shu und Ruilin Jiang. „Dilated convolution based botnet detection model“. In Third International Conference on Computer Communication and Network Security (CCNS 2022), herausgegeben von Chuanjun Zhao und Hilal Imane. SPIE, 2022. http://dx.doi.org/10.1117/12.2659107.
Der volle Inhalt der QuelleG, Sakthi Priya, und Padmapriya N. „Texture Image Classification with Dilated Convolution Layers“. In 2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET). IEEE, 2023. http://dx.doi.org/10.1109/wispnet57748.2023.10133964.
Der volle Inhalt der QuellePan, Shunan, Juan Du, Haonan Yu, Yuhan Cheng, Liye Mei, Chuan Xu und Wei Yang. „Dilated Convolution Network for Road Damage Detection“. In 2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS). IEEE, 2023. http://dx.doi.org/10.1109/isctis58954.2023.10213051.
Der volle Inhalt der QuelleZhou, Shengwei, Caikou Chen, Guojiang Han und Xielian Hou. „Deep Convolutional Neural Network with Dilated Convolution Using Small Size Dataset“. In 2019 Chinese Control Conference (CCC). IEEE, 2019. http://dx.doi.org/10.23919/chicc.2019.8865226.
Der volle Inhalt der QuelleWu, Lin (Yuanbo), Deyin Liu, Xiaojie Guo, Richang Hong, Liangchen Liu und Rui Zhang. „Multi-scale Spatial Representation Learning via Recursive Hermite Polynomial Networks“. In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/204.
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