Literatura académica sobre el tema "Convolutional Auto-Encoder"
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Artículos de revistas sobre el tema "Convolutional Auto-Encoder"
Song, Xiaona, Haichao Liu, Lijun Wang, Song Wang, Yunyu Cao, Donglai Xu y Shenfeng Zhang. "A Semantic Segmentation Method for Road Environment Images Based on Hybrid Convolutional Auto-Encoder". Traitement du Signal 39, n.º 4 (31 de agosto de 2022): 1235–45. http://dx.doi.org/10.18280/ts.390416.
Texto completoKim, Dong-Hoon, JoonWhoan Lee y #VALUE! #VALUE! "Music Mood recognition using Convolutional Variation Auto Encoder". Journal of Korean Institute of Intelligent Systems 29, n.º 5 (31 de octubre de 2019): 352–58. http://dx.doi.org/10.5391/jkiis.2019.29.5.352.
Texto completoTheunissen, Carl Daniel, Steven Martin Bradshaw, Lidia Auret y Tobias Muller Louw. "One-Dimensional Convolutional Auto-Encoder for Predicting Furnace Blowback Events from Multivariate Time Series Process Data—A Case Study". Minerals 11, n.º 10 (9 de octubre de 2021): 1106. http://dx.doi.org/10.3390/min11101106.
Texto completoYasukawa, Shinsuke, Sreeraman Raghura, Yuya Nishida y Kazuo Ishii. "Underwater image reconstruction using convolutional auto-encoder". Proceedings of International Conference on Artificial Life and Robotics 26 (21 de enero de 2021): 262–65. http://dx.doi.org/10.5954/icarob.2021.os23-4.
Texto completoZhao, Wei, Zuchen Jia, Xiaosong Wei y Hai Wang. "An FPGA Implementation of a Convolutional Auto-Encoder". Applied Sciences 8, n.º 4 (27 de marzo de 2018): 504. http://dx.doi.org/10.3390/app8040504.
Texto completoLi, Hongfei, Lili Meng, Jia Zhang, Yanyan Tan, Yuwei Ren y Huaxiang Zhang. "Multiple Description Coding Based on Convolutional Auto-Encoder". IEEE Access 7 (2019): 26013–21. http://dx.doi.org/10.1109/access.2019.2900498.
Texto completoNewlin, Dev R. y C. Seldev Christopher. "De-noising of Natural Images with Better Enhancement Using Convolutional Auto-Encoder". Journal of Advanced Research in Dynamical and Control Systems 11, n.º 12 (20 de diciembre de 2019): 124–36. http://dx.doi.org/10.5373/jardcs/v11i12/20193221.
Texto completoZhu, Yi, Lei Li y Xindong Wu. "Stacked Convolutional Sparse Auto-Encoders for Representation Learning". ACM Transactions on Knowledge Discovery from Data 15, n.º 2 (abril de 2021): 1–21. http://dx.doi.org/10.1145/3434767.
Texto completoZhou, Jian, Xianwei Wei, Chunling Cheng, Qidong Yang y Qun Li. "Multimodal Emotion Recognition Method Based on Convolutional Auto-Encoder". International Journal of Computational Intelligence Systems 12, n.º 1 (2019): 351. http://dx.doi.org/10.2991/ijcis.2019.125905651.
Texto completoOh, Junghyun y Beomhee Lee. "Condition-invariant Place Recognition Using Deep Convolutional Auto-encoder". Journal of Korea Robotics Society 14, n.º 1 (30 de marzo de 2019): 8–13. http://dx.doi.org/10.7746/jkros.2019.14.1.008.
Texto completoTesis sobre el tema "Convolutional Auto-Encoder"
Ionascu, Beatrice. "Modelling user interaction at scale with deep generative methods". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239333.
Texto completoFörståelse för hur användare interagerar med ett företags tjänst är essentiell för data-drivna affärsverksamheter med ambitioner om att bättre tillgodose dess användare och att förbättra deras utbud. Generativ maskininlärning möjliggör modellering av användarbeteende och genererande av ny data i syfte att simulera eller identifiera och förklara typiska användarmönster. I detta arbete introducerar vi ett tillvägagångssätt för storskalig modellering av användarinteraktion i en klientservice-modell. Vi föreslår en ny representation av multivariat tidsseriedata i form av tidsbilder vilka representerar temporala korrelationer via spatial organisering. Denna representation delar två nyckelegenskaper som faltningsnätverk har utvecklats för att exploatera, vilket tillåter oss att utveckla ett tillvägagångssätt baserat på på djupa generativa modeller som bygger på faltningsnätverk. Genom att introducera detta tillvägagångssätt för tidsseriedata expanderar vi applicering av faltningsnätverk inom domänen för multivariat tidsserie, specifikt för användarinteraktionsdata. Vi använder ett tillvägagångssätt inspirerat av ramverket β-VAE i syfte att lära modellen gömda faktorer som definierar olika användarmönster. Vi utforskar olika värden för regulariseringsparametern β och visar att det är möjligt att konstruera en modell som lär sig en latent representation av identifierbara och multipla användarbeteenden. Vi visar med verklig data att modellen genererar realistiska exempel vilka i sin tur fångar statistiken på populationsnivå hos användarinteraktionsdatan, samt lär olika användarbeteenden och bidrar med precisa imputationer av saknad data.
Sayadi, Karim. "Classification du texte numérique et numérisé. Approche fondée sur les algorithmes d'apprentissage automatique". Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066079/document.
Texto completoDifferent disciplines in the humanities, such as philology or palaeography, face complex and time-consuming tasks whenever it comes to examining the data sources. The introduction of computational approaches in humanities makes it possible to address issues such as semantic analysis and systematic archiving. The conceptual models developed are based on algorithms that are later hard coded in order to automate these tedious tasks. In the first part of the thesis we propose a novel method to build a semantic space based on topics modeling. In the second part and in order to classify historical documents according to their script. We propose a novel representation learning method based on stacking convolutional auto-encoder. The goal is to automatically learn plot representations of the script or the written language
Chen, Peng-Cheng y 陳鵬丞. "Semi-Supervised Learning Framework with an Auto-Elastic Convolutional Auto-Encoder for Image Classification Design". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/sv6v8s.
Texto completo逢甲大學
自動控制工程學系
106
More and more attention of the deep learning for image and speech recognition, even artificial intelligence (AI) beats humans in ancient game of Go has been attracted with the development of computer and electronic devices. In neural networks, deep learning with convolutional neural network (CNN) has been widely utilized in numerous applications of automatic image recognition, such as multiple object detection and classification, object tracking, sematic segmentation, and most of them have achieved outstanding results. However, with the applications are becoming more complex and various deep learning frameworks are constantly evolving, the neural networks need multiple layers in order to learn more detailed and more abstractions relationships within the data, and then it can enhance the efficiency of the recognition. Some popular types of deep neural networks often requires lots of labeled data as well as huge computing power and for this reason they are not easily to be integrated into embedded system. Indeed, sometimes problems don't need more layer neural networks to solve it, the relatively simple environment may have achieved better results using first few layers merely. Thus, it causes that the excess memory goes to waste and tedious and time-consuming process. Hence, the thesis presents a novel semi-supervised learning framework with an auto-elastic convolutional auto-encoder for image detection and classification. The proposed an auto-elastic convolutional auto-encoder learning framework is based on the Actor-Critic reinforcement learning approach. The Actor-Critic is the learning of a mapping from situations to actions, which learns from the consequences of its actions, rather than from being unequivocally instructed and it selects its actions on basis of its past experiences and also by new choices. It is a trial and error learning essentially. The main contribution of this study is to repeatedly generate the convolution kernels based on Actor-Critic algorithms and then determine the optimal number of layers of neural network framework so that the whole neural network framework can achieve self-elasticity function depending on task complexity. The novel Auto-Elastic Convolutional Auto-Encoder framework is mainly divided into four parts: auto-elastic unit, data generation unit, memory unit, learner unit. First, the auto-elastic unit is based on the proposed Actor-Critic encoder (ACE) algorithms. The Actor generates a coding policy (action) by using current image/feature map and historical convolution kernels information. The Critic as a value estimator, whereas the actor attempts to select actions (coding policy) based on the value function estimated by the critic. Next, memory unit is used to store the coding results and historical information from the auto-elastic unit, such as corresponding rewards, historical convolution kernels, feature maps, etc., and combine a data generation unit to generate training sample data automatically. Final, the learner unit is proposed as for the proposed semi-supervised learning framework training. Extensively simulation, quantitative analysis and comparison results demonstrate the feasibility and efficiency of the proposed auto-elastic convolutional auto-encoder learning framework for digit recognition and classification.
Liao, Chi-Jou y 廖綺柔. "Using Convolutional Neural Network Auto-encoder in Breast Tumors Classification and Detection Compare with Traditional Ultrasound BIRADS". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/tfz345.
Texto completoPaul, Subir. "Hyperspectral Remote Sensing for Land Cover Classification and Chlorophyll Content Estimation using Advanced Machine Learning Techniques". Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4537.
Texto completoCapítulos de libros sobre el tema "Convolutional Auto-Encoder"
Chen, Wei, Ruimin Hu, Xiaochen Wang y Dengshi Li. "HRTF Representation with Convolutional Auto-encoder". En MultiMedia Modeling, 605–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37731-1_49.
Texto completoWang, Taizheng, Chunyang Ye, Hui Zhou, Mingwang Ou y Bo Cheng. "AIS Ship Trajectory Clustering Based on Convolutional Auto-encoder". En Advances in Intelligent Systems and Computing, 529–46. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55187-2_39.
Texto completoWang, Diangang, Wei Gan, Chenyang Yan, Kun Huang y Hongyi Wu. "Inception Model of Convolutional Auto-encoder for Image Denoising". En Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 174–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64214-3_12.
Texto completoShamsi, Meysam, Damien Lolive, Nelly Barbot y Jonathan Chevelu. "Script Selection Using Convolutional Auto-encoder for TTS Speech Corpus". En Speech and Computer, 423–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26061-3_43.
Texto completoXu, Chaoyang, Ling Wu y Shiping Wang. "Unsupervised Dimension Reduction for Image Classification Using Regularized Convolutional Auto-Encoder". En Advances in Intelligent Systems and Computing, 99–108. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17795-9_8.
Texto completoHan, Xiaobing, Yanfei Zhong, Lifang He, Philip S. Yu y Liangpei Zhang. "The Unsupervised Hierarchical Convolutional Sparse Auto-Encoder for Neuroimaging Data Classification". En Brain Informatics and Health, 156–66. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23344-4_16.
Texto completoArslan, Abdullah Taha y Ugur Yayan. "Convolutional Auto-Encoder Based Degradation Point Forecasting for Bearing Data Set". En Artificial Intelligence and Applied Mathematics in Engineering Problems, 817–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36178-5_71.
Texto completoXiang, Xinyu, Ping Zhang, Qiang Yuan, Renping Li, Runqiao Hu y Ke Li. "Few-Shot Learning Based on Convolutional Denoising Auto-encoder Relational Network". En Communications in Computer and Information Science, 103–12. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9247-5_8.
Texto completoLi, Chun, Wenfeng Shi y Lin Shang. "Latent Feature Representation for Cohesive Community Detection Based on Convolutional Auto-Encoder". En Big Data, 380–94. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1899-7_27.
Texto completoShuvo, M. I. R., M. A. H. Akhand y N. Siddique. "Handwritten Numeral Superposition to Printed Form Using Convolutional Auto-Encoder and Recognition Using Convolutional Neural Network". En Proceedings of International Joint Conference on Computational Intelligence, 179–90. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3607-6_14.
Texto completoActas de conferencias sobre el tema "Convolutional Auto-Encoder"
Ye, Hao, Le Liang y Geoffrey Ye Li. "Circular Convolutional Auto-Encoder for Channel Coding". En 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2019. http://dx.doi.org/10.1109/spawc.2019.8815483.
Texto completoSharma, Manish, Panos P. Markopoulos, Eli Saber, M. Salman Asif y Ashley Prater-Bennette. "Convolutional Auto-Encoder with Tensor-Train Factorization". En 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2021. http://dx.doi.org/10.1109/iccvw54120.2021.00027.
Texto completoWu, Hao, Ziyang Zheng, Yong Li, Wenrui Dai y Hongkai Xiong. "Compressed Sensing via a Deep Convolutional Auto-encoder". En 2018 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2018. http://dx.doi.org/10.1109/vcip.2018.8698640.
Texto completoAlqahtani, A., X. Xie, J. Deng y M. W. Jones. "A Deep Convolutional Auto-Encoder with Embedded Clustering". En 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451506.
Texto completoTurchenko, Volodymyr y Artur Luczak. "Creation of a deep convolutional auto-encoder in Caffe". En 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2017. http://dx.doi.org/10.1109/idaacs.2017.8095172.
Texto completoBaccouche, Moez, Franck Mamalet, Christian Wolf, Christophe Garcia y Atilla Baskurt. "Spatio-Temporal Convolutional Sparse Auto-Encoder for Sequence Classification". En British Machine Vision Conference 2012. British Machine Vision Association, 2012. http://dx.doi.org/10.5244/c.26.124.
Texto completoSchuch, Patrick, Simon Schulz y Christoph Busch. "De-convolutional auto-encoder for enhancement of fingerprint samples". En 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2016. http://dx.doi.org/10.1109/ipta.2016.7821036.
Texto completoZuo, Haolan. "CDAE-C: A Fully Convolutional Denoising Auto-Encoder with 2.5D Convolutional Classifier". En 2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). IEEE, 2022. http://dx.doi.org/10.1109/tocs56154.2022.10015922.
Texto completoGeng, Chi y JianXin Song. "Human Action Recognition based on Convolutional Neural Networks with a Convolutional Auto-Encoder". En 2015 5th International Conference on Computer Sciences and Automation Engineering (ICCSAE 2015). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/iccsae-15.2016.173.
Texto completoTian, Sirui, Chao Wang y Hong Zhang. "SAR Object Classification with a Multi-Scale Convolutional Auto-Encoder". En 2019 SAR in Big Data Era (BIGSARDATA). IEEE, 2019. http://dx.doi.org/10.1109/bigsardata.2019.8858491.
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