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Статті в журналах з теми "Convolutional transformer"
Li, Pengfei, Peixiang Zhong, Kezhi Mao, Dongzhe Wang, Xuefeng Yang, Yunfeng Liu, Jianxiong Yin, and Simon See. "ACT: an Attentive Convolutional Transformer for Efficient Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 15 (May 18, 2021): 13261–69. http://dx.doi.org/10.1609/aaai.v35i15.17566.
Повний текст джерелаHe, Ping, Yong Li, Shoulong Chen, Hoghua Xu, Lei Zhu, and Lingyan Wang. "Core looseness fault identification model based on Mel spectrogram-CNN." Journal of Physics: Conference Series 2137, no. 1 (December 1, 2021): 012060. http://dx.doi.org/10.1088/1742-6596/2137/1/012060.
Повний текст джерелаLi, Xiaopeng, and Shuqin Li. "Transformer Help CNN See Better: A Lightweight Hybrid Apple Disease Identification Model Based on Transformers." Agriculture 12, no. 6 (June 19, 2022): 884. http://dx.doi.org/10.3390/agriculture12060884.
Повний текст джерелаZhou, Li, Tongqin Shi, Songquan Huang, Fangchao Ke, Zhenxi Huang, Zhaoyang Zhang, and Jinzheng Liang. "Convolutional neural network for real-time main transformer detection." Journal of Physics: Conference Series 2229, no. 1 (March 1, 2022): 012021. http://dx.doi.org/10.1088/1742-6596/2229/1/012021.
Повний текст джерелаZhang, Zhiwen, Teng Li, Xuebin Tang, Xiang Hu, and Yuanxi Peng. "CAEVT: Convolutional Autoencoder Meets Lightweight Vision Transformer for Hyperspectral Image Classification." Sensors 22, no. 10 (May 20, 2022): 3902. http://dx.doi.org/10.3390/s22103902.
Повний текст джерелаXu, Jun, Zi-Xuan Chen, Hao Luo, and Zhe-Ming Lu. "An Efficient Dehazing Algorithm Based on the Fusion of Transformer and Convolutional Neural Network." Sensors 23, no. 1 (December 21, 2022): 43. http://dx.doi.org/10.3390/s23010043.
Повний текст джерелаYang, Liming, Yihang Yang, Jinghui Yang, Ningyuan Zhao, Ling Wu, Liguo Wang, and Tianrui Wang. "FusionNet: A Convolution–Transformer Fusion Network for Hyperspectral Image Classification." Remote Sensing 14, no. 16 (August 19, 2022): 4066. http://dx.doi.org/10.3390/rs14164066.
Повний текст джерелаIbrahem, Hatem, Ahmed Salem, and Hyun-Soo Kang. "RT-ViT: Real-Time Monocular Depth Estimation Using Lightweight Vision Transformers." Sensors 22, no. 10 (May 19, 2022): 3849. http://dx.doi.org/10.3390/s22103849.
Повний текст джерелаWu, Jiajing, Zhiqiang Wei, Jinpeng Zhang, Yushi Zhang, Dongning Jia, Bo Yin, and Yunchao Yu. "Full-Coupled Convolutional Transformer for Surface-Based Duct Refractivity Inversion." Remote Sensing 14, no. 17 (September 3, 2022): 4385. http://dx.doi.org/10.3390/rs14174385.
Повний текст джерелаSowndarya, S., and Sujatha Balaraman. "Diagnosis of Partial Discharge in Power Transformer using Convolutional Neural Network." March 2022 4, no. 1 (April 30, 2022): 29–38. http://dx.doi.org/10.36548/jscp.2022.1.004.
Повний текст джерелаДисертації з теми "Convolutional transformer"
Dronzeková, Michaela. "Analýza polygonálních modelů pomocí neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417253.
Повний текст джерелаDomini, Davide. "Classificazione di Radiografie Toraciche per la Diagnosi del COVID-19 con Reti Convoluzionali e Vision Transformer." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24243/.
Повний текст джерелаRichtarik, Lukáš. "Rozpoznávání ručně psaného textu pomocí hlubokých neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-433517.
Повний текст джерелаDuhamel, Pierre. "Algorithmes de transformées discrètes rapides pour convolution cyclique et de convolution cyclique pour transformées rapides." Paris 11, 1986. http://www.theses.fr/1986PA112197.
Повний текст джерелаFirst, we present the fast transforms to be considered in the following (Number Theoretic Transforms - NTT -, Fast Fourier Transforms - FFT -, Polynomial Transforms - PT -, Discrete Cosine Transforms - OCT -) in a unified manner. Then, we use the link between NTT and PT to propose a new family of NTT with 2 as a root of unity which includes classical ones (Fermat, Mersenne, etc. . . ), but also includes new ones, which allow transforming longer sequences for a given dynamic range. We also establish a decomposition property of the arithmetic in this family of NTT's. We also propose a set of NTT algorithms with minimum number of shifts. Next, for the computation of FFT's, we introduced the "split-radix" algorithm. Application of this algorithm to complex, real or real-symmetric data requires in each case the smallest known number of operations (multiplications and additions), with a regular structure. We also showed that any improvement of one of these algorithms would also bring a corresponding improvement of the other ones. Furthermore, we showed that their structure is very similar to that of the optimum (minimum number of multiplications) algorithm. The search for minimum multiplication DCT algorithms enabled us to show the equivalence between a 2n DCT and a cyclic convolution. We used this equivalence to propose two new architectures for DCT computation. The first one presents the advantage of needing only one general multiplier per point, but necessitates modulo-arithmetic. The second one, based on distributed arithmetic, has a very simple structure that can be used also for other fast transforms (FFT, e. G. )
Kwok, Yien Chian. "Shah convolution Fourier transform detection." Thesis, Imperial College London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.428105.
Повний текст джерелаWestermark, Pontus. "Wavelets, Scattering transforms and Convolutional neural networks : Tools for image processing." Thesis, Uppsala universitet, Analys och sannolikhetsteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-337570.
Повний текст джерелаHighlander, Tyler. "Efficient Training of Small Kernel Convolutional Neural Networks using Fast Fourier Transform." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1432747175.
Повний текст джерелаMartucci, Stephen A. "Symmetric convolution and the discrete sine and cosine transforms : principles and applications." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/15038.
Повний текст джерелаDušanka, Perišić. "On Integral Transforms and Convolution Equations on the Spaces of Tempered Ultradistributions." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 1992. https://www.cris.uns.ac.rs/record.jsf?recordId=73337&source=NDLTD&language=en.
Повний текст джерелаU ovoj tezi su proučavani prostori temperiranih ultradistribucija Beurlingovog i Roumieovog tipa, koji su prirodna uopštenja prostora Schwarzovih temperiranih distribucija u Denjoy-Carleman-Komatsuovoj teoriji ultradistribucija. Dokazano je ovi prostori imaju sva dobra svojstva, koja ima i Schwarzov prostor, izmedju ostalog, značajno svojstvo da Furijeova transformacija preslikava te prostore neprekidno na same sebe.U prvom poglavlju su uvedene neophodne oznake i pojmovi.U drugom poglavlju su uvedeni prostori ultrabrzo opadajucih ultradiferencijabilnih funkcija i njihovi duali, prostori Beurlingovih i Rumieuovih temperiranih ultradistribucija; proučavana su njihova topološka svojstva i veze sa poznatim prostorima distribucija i ultradistribucija, kao i strukturne osobine; date su i karakterizacije Ermitskih ekspanzija i graničnih reprezentacija elemenata tih prostora.Prostori multiplikatora Beurlingovih i Roumieuovih temperiranih ultradistribucija su okarakterisani u trećem poglavlju.Četvrto poglavlje je posvećeno proučavanju Fourierove, Wignerove, Bargmanove i Hilbertove transformacije na prostorima Beurlingovih i Rouimieovih temperiranih ultradistribucija i njihovim test prostorima.U petoj glavi je dokazana ekvivalentnost klasičnih definicija konvolucije na Beurlingovim prostorima ultradistribucija, kao i ekvivalentnost novouvedenih definicija ultratemperirane konvolucije ultradistribucija Beurlingovog tipa.U poslednjoj glavi je dat potreban i dovoljan uslov da konvolutor prostora temperiranih ultradistribucija bude hipoeliptičan u prostoru integrabilnih ultradistribucija i razmatrane su neke konvolucione jednačine u tom prostoru.Bibliografija ima 70 bibliografskih jedinica.
Marir, F. "The application of number theoretic transforms to two dimensional convolutions and adaptive filtering." Thesis, University of Newcastle Upon Tyne, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.374136.
Повний текст джерелаКниги з теми "Convolutional transformer"
Yakubovich, S. B. The hypergeometric approach to integral transforms and convolutions. Dordrecht: Kluwer Academic, 1994.
Знайти повний текст джерелаConvolution and equidistribution: Sato-Tate theorems for finite-field Mellin transforms. Princeton: Princeton University Press, 2012.
Знайти повний текст джерелаLaamri, El Haj. Mesures, integration, convolution, et transformee de Fourier des fonctions. Dunod, 2001.
Знайти повний текст джерелаЧастини книг з теми "Convolutional transformer"
Durall, Ricard, Stanislav Frolov, Jörn Hees, Federico Raue, Franz-Josef Pfreundt, Andreas Dengel, and Janis Keuper. "Combining Transformer Generators with Convolutional Discriminators." In KI 2021: Advances in Artificial Intelligence, 67–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87626-5_6.
Повний текст джерелаWang, Cong, Hongmin Xu, Xiong Zhang, Li Wang, Zhitong Zheng, and Haifeng Liu. "Convolutional Embedding Makes Hierarchical Vision Transformer Stronger." In Lecture Notes in Computer Science, 739–56. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20044-1_42.
Повний текст джерелаWu, Jia, Hong-zhen Yang, Hong-fei Xu, Si-ri Pang, Huan-yuan Li, and Wang Luo. "Attribute Segmentation for Transformer Substation Using Convolutional Network." In Advances in Intelligent Systems and Computing, 679–86. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30874-6_63.
Повний текст джерелаEstupiñán-Ojeda, Cristian, Cayetano Guerra-Artal, and Mario Hernández-Tejera. "Informer: An Efficient Transformer Architecture Using Convolutional Layers." In Lecture Notes in Computer Science, 208–17. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10161-8_11.
Повний текст джерелаLiu, Yuqi, Yin Wang, Haikuan Du, and Shen Cai. "Spherical Transformer: Adapting Spherical Signal to Convolutional Networks." In Pattern Recognition and Computer Vision, 15–27. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18913-5_2.
Повний текст джерелаJain, Kushal, Fenil Doshi, and Lakshmi Kurup. "Stance Detection Using Transformer Architectures and Temporal Convolutional Networks." In Advances in Computer, Communication and Computational Sciences, 437–47. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4409-5_40.
Повний текст джерелаWoon, Wei Lee, Zeyar Aung, and Ayman El-Hag. "Intelligent Monitoring of Transformer Insulation Using Convolutional Neural Networks." In Data Analytics for Renewable Energy Integration. Technologies, Systems and Society, 127–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04303-2_10.
Повний текст джерелаWu, Jia, Dan Su, Hong-fei Xu, Si-ri Pang, and Wang Luo. "Attribute Classification for Transformer Substation Based on Deep Convolutional Network." In Advances in Intelligent Systems and Computing, 669–77. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30874-6_62.
Повний текст джерелаLin, Ailiang, Jiayu Xu, Jinxing Li, and Guangming Lu. "ConTrans: Improving Transformer with Convolutional Attention for Medical Image Segmentation." In Lecture Notes in Computer Science, 297–307. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16443-9_29.
Повний текст джерелаYang, Zi-An, Nai-Rong Zheng, and Feng Wang. "SAR Image Classification by Combining Transformer and Convolutional Neural Networks." In Lecture Notes in Electrical Engineering, 193–200. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8202-6_18.
Повний текст джерелаТези доповідей конференцій з теми "Convolutional transformer"
Zeng, Kungan, and Incheon Paik. "A Lightweight Transformer with Convolutional Attention." In 2020 11th International Conference on Awareness Science and Technology (iCAST). IEEE, 2020. http://dx.doi.org/10.1109/icast51195.2020.9319489.
Повний текст джерелаLuo, Ge, Ping Wei, Shuwen Zhu, Xinpeng Zhang, Zhenxing Qian, and Sheng Li. "Image Steganalysis with Convolutional Vision Transformer." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747091.
Повний текст джерелаKe, Nan, Tong Lin, Zhouchen Lin, Xiao-Hua Zhou, and Taoyun Ji. "Convolutional Transformer Networks for Epileptic Seizure Detection." In CIKM '22: The 31st ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3511808.3557568.
Повний текст джерелаLee, Sihaeng, Eojindl Yi, Janghyeon Lee, Jinsu Yoo, Honglak Lee, and Seung Hwan Kim. "Fully Convolutional Transformer with Local-Global Attention." In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9981339.
Повний текст джерелаHuang, Chao, Jiashu Zhao, and Dawei Yin. "Purchase Intent Forecasting with Convolutional Hierarchical Transformer Networks." In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. http://dx.doi.org/10.1109/icde51399.2021.00281.
Повний текст джерелаPrayuda, Alim Wicaksono Hari, Heri Prasetyo, and Jing-Ming Guo. "AWGN-Based Image Denoiser using Convolutional Vision Transformer." In 2021 International Symposium on Electronics and Smart Devices (ISESD). IEEE, 2021. http://dx.doi.org/10.1109/isesd53023.2021.9501567.
Повний текст джерелаAlam, Mohammad Mahabub, Gour Karmakar, Syed Islam, Joarder Kamruzzaman, Madhu Chetty, Suryani Lim, Gayan Appuhamillage, Gopi Chattopadhyay, Steve Wilcox, and Vincent Verheyen. "Assessing Transformer Oil Quality using Deep Convolutional Networks." In 2019 29th Australasian Universities Power Engineering Conference (AUPEC). IEEE, 2019. http://dx.doi.org/10.1109/aupec48547.2019.211896.
Повний текст джерелаYe, Longqing. "Dynamic Clone Transformer for Efficient Convolutional Neural Netwoks." In ICCAI '22: 2022 8th International Conference on Computing and Artificial Intelligence. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3532213.3532222.
Повний текст джерелаBai, Ruwen, Min Li, Bo Meng, Fengfa Li, Miao Jiang, Junxing Ren, and Degang Sun. "Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition." In 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2022. http://dx.doi.org/10.1109/icme52920.2022.9859781.
Повний текст джерелаWu, Zhenyu, Chaohui Song, and Haibin Yan. "TC-Net: Transformer-Convolutional Networks for Road Segmentation." In 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2022. http://dx.doi.org/10.1109/icme52920.2022.9859734.
Повний текст джерелаЗвіти організацій з теми "Convolutional transformer"
Foltz, Thomas M. Symmetric Convolution. Using Unitary Transform Matrices: A New Approach to Image Reconstruction. Fort Belvoir, VA: Defense Technical Information Center, April 1999. http://dx.doi.org/10.21236/ada389062.
Повний текст джерелаNuttall, Albert H. Two-Dimensional Convolutions, Correlations, and Fourier Transforms of Combinations of Wigner Distribution Functions and Complex Ambiguity Functions. Fort Belvoir, VA: Defense Technical Information Center, August 1990. http://dx.doi.org/10.21236/ada226852.
Повний текст джерела