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Статті в журналах з теми "Deep Unsupervised Learning"
Zhao, Tingting, Zifeng Wang, Aria Masoomi, and Jennifer Dy. "Deep Bayesian Unsupervised Lifelong Learning." Neural Networks 149 (May 2022): 95–106. http://dx.doi.org/10.1016/j.neunet.2022.02.001.
Повний текст джерелаBanzi, Jamal, Isack Bulugu, and Zhongfu Ye. "Deep Predictive Neural Network: Unsupervised Learning for Hand Pose Estimation." International Journal of Machine Learning and Computing 9, no. 4 (August 2019): 432–39. http://dx.doi.org/10.18178/ijmlc.2019.9.4.822.
Повний текст джерелаFong, A. C. M., and G. Hong. "Boosted Supervised Intensional Learning Supported by Unsupervised Learning." International Journal of Machine Learning and Computing 11, no. 2 (March 2021): 98–102. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1020.
Повний текст джерелаHuang, Jiabo, Qi Dong, Shaogang Gong, and Xiatian Zhu. "Unsupervised Deep Learning via Affinity Diffusion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11029–36. http://dx.doi.org/10.1609/aaai.v34i07.6757.
Повний текст джерелаSanakoyeu, Artsiom, Miguel A. Bautista, and Björn Ommer. "Deep unsupervised learning of visual similarities." Pattern Recognition 78 (June 2018): 331–43. http://dx.doi.org/10.1016/j.patcog.2018.01.036.
Повний текст джерелаYousefi-Azar, Mahmood, and Len Hamey. "Text summarization using unsupervised deep learning." Expert Systems with Applications 68 (February 2017): 93–105. http://dx.doi.org/10.1016/j.eswa.2016.10.017.
Повний текст джерелаLiu, Dong, Chengjian Sun, Chenyang Yang, and Lajos Hanzo. "Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning." IEEE Network 34, no. 4 (July 2020): 270–77. http://dx.doi.org/10.1109/mnet.001.1900517.
Повний текст джерелаXuejun Zhang, Xuejun Zhang, Jiyang Gai Xuejun Zhang, Zhili Ma Jiyang Gai, Jinxiong Zhao Zhili Ma, Hongzhong Ma Jinxiong Zhao, Fucun He Hongzhong Ma, and Tao Ju Fucun He. "Exploring Unsupervised Learning with Clustering and Deep Autoencoder to Detect DDoS Attack." 電腦學刊 33, no. 4 (August 2022): 029–44. http://dx.doi.org/10.53106/199115992022083304003.
Повний текст джерелаKim, Seonghyeon, Sunjin Jung, Kwanggyoon Seo, Roger Blanco i Ribera, and Junyong Noh. "Deep Learning‐Based Unsupervised Human Facial Retargeting." Computer Graphics Forum 40, no. 7 (October 2021): 45–55. http://dx.doi.org/10.1111/cgf.14400.
Повний текст джерелаLi, Changsheng, Rongqing Li, Ye Yuan, Guoren Wang, and Dong Xu. "Deep Unsupervised Active Learning via Matrix Sketching." IEEE Transactions on Image Processing 30 (2021): 9280–93. http://dx.doi.org/10.1109/tip.2021.3124317.
Повний текст джерелаДисертації з теми "Deep Unsupervised Learning"
Drexler, Jennifer Fox. "Deep unsupervised learning from speech." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105696.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 87-92).
Automatic speech recognition (ASR) systems have become hugely successful in recent years - we have become accustomed to speech interfaces across all kinds of devices. However, despite the huge impact ASR has had on the way we interact with technology, it is out of reach for a significant portion of the world's population. This is because these systems rely on a variety of manually-generated resources - like transcripts and pronunciation dictionaries - that can be both expensive and difficult to acquire. In this thesis, we explore techniques for learning about speech directly from speech, with no manually generated transcriptions. Such techniques have the potential to revolutionize speech technologies for the vast majority of the world's population. The cognitive science and computer science communities have both been investing increasing time and resources into exploring this problem. However, a full unsupervised speech recognition system is a hugely complicated undertaking and is still a long ways away. As in previous work, we focus on the lower-level tasks which will underlie an eventual unsupervised speech recognizer. We specifically focus on two tasks: developing linguistically meaningful representations of speech and segmenting speech into phonetic units. This thesis approaches these tasks from a new direction: deep learning. While modern deep learning methods have their roots in ideas from the 1960s and even earlier, deep learning techniques have recently seen a resurgence, thanks to huge increases in computational power and new efficient learning algorithms. Deep learning algorithms have been instrumental in the recent progress of traditional supervised speech recognition; here, we extend that work to unsupervised learning from speech.
by Jennifer Fox Drexler.
S.M.
Ahn, Euijoon. "Unsupervised Deep Feature Learning for Medical Image Analysis." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/23002.
Повний текст джерелаCaron, Mathilde. "Unsupervised Representation Learning with Clustering in Deep Convolutional Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-227926.
Повний текст джерелаDetta examensarbete behandlar problemet med oövervakat lärande av visuella representationer med djupa konvolutionella neurala nätverk (CNN). Detta är en av de viktigaste faktiska utmaningarna i datorseende för att överbrygga klyftan mellan oövervakad och övervakad representationstjänst. Vi föreslår ett nytt och enkelt sätt att träna CNN på helt omärkta dataset. Vår metod består i att tillsammans optimera en gruppering av representationerna och träna ett CNN med hjälp av grupperna som tillsyn. Vi utvärderar modellerna som tränats med vår metod på standardöverföringslärande experiment från litteraturen. Vi finner att vår metod överträffar alla självövervakade och oövervakade, toppmoderna tillvägagångssätt, hur sofistikerade de än är. Ännu viktigare är att vår metod överträffar de metoderna även när den oövervakade träningsuppsättningen inte är ImageNet men en godtycklig delmängd av bilder från Flickr.
Manjunatha, Bharadwaj Sandhya. "Land Cover Quantification using Autoencoder based Unsupervised Deep Learning." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99861.
Повний текст джерелаMaster of Science
This work aims to develop an automated deep learning model for identifying and estimating the composition of the different land covers in a region using hyperspectral remote sensing imagery. With the technological advancements in remote sensing, hyperspectral imagery which captures high resolution images of the earth's surface across hundreds of wavelength bands, is becoming increasingly popular. As every surface has a unique reflectance pattern, the high spectral information contained in these images can be analyzed to identify the various target materials present in the image scene. An autoencoder is a deep learning model that can perform spectral unmixing by decomposing the complex image spectra into its constituent materials and estimate their percent compositions. The advantage of this method in land cover quantification is that it is an unsupervised technique which does not require labelled data which generally requires years of field survey and formulation of detailed maps. The performance of this technique is evaluated on various synthetic and real hyperspectral datasets consisting of different land covers. We assess the scalability of the model by evaluating its performance on images of different sizes spanning over a few hundred square meters to thousands of square meters. Finally, we compare the performance of the autoencoder based approach with other supervised and unsupervised deep learning techniques and with the current land cover classification standard.
Martin, Damien W. "Fault detection in manufacturing equipment using unsupervised deep learning." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130698.
Повний текст джерелаCataloged from the official PDF of thesis.
Includes bibliographical references (pages 87-90).
We investigate the use of unsupervised deep learning to create a general purpose automated fault detection system for manufacturing equipment. Unexpected equipment faults can be costly to manufacturing lines, but data driven fault detection systems often require a high level of application specific expertise to implement and continued human oversight. Collecting large labeled datasets to train such a system can also be challenging due to the sparse nature of faults. To address this, we focus on unsupervised deep learning approaches, and their ability to generalize across applications without changes to the hyper-parameters or architecture. Previous work has demonstrated the efficacy of autoencoders in unsupervised anomaly detection systems. In this work we propose a novel variant of the deep auto-encoding Gaussian mixture model, optimized for time series applications, and test its efficacy in detecting faults across a range of manufacturing equipment. It was tested against fault datasets from three milling machines, two plasma etchers, and one spinning ball bearing. In our tests, the model is able to detect over 80% of faults in all cases without the use of labeled data and without hyperparameter changes between applications. We also find that the model is capable of classifying different failure modes in some of our tests, and explore other ways the system can be used to provide useful diagnostic information. We present preliminary results from a continual learning variant of our fault detection architecture aimed at tackling the problem of system drift.
by Damien W. Martin.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Liu, Dongnan. "Supervised and Unsupervised Deep Learning-based Biomedical Image Segmentation." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24744.
Повний текст джерелаNasrin, Mst Shamima. "Pathological Image Analysis with Supervised and Unsupervised Deep Learning Approaches." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620052562772676.
Повний текст джерелаWu, Xinheng. "A Deep Unsupervised Anomaly Detection Model for Automated Tumor Segmentation." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22502.
Повний текст джерелаLängkvist, Martin. "Modeling time-series with deep networks." Doctoral thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-39415.
Повний текст джерелаDekhtiar, Jonathan. "Deep Learning and unsupervised learning to automate visual inspection in the manufacturing industry." Thesis, Compiègne, 2019. http://www.theses.fr/2019COMP2513.
Повний текст джерелаAlthough studied since 1970, automatic visual inspection on production lines still struggles to be applied on a large scale and at low cost. The methods used depend greatly on the availability of domain experts. This inevitably leads to increased costs and reduced flexibility in the methods used. Since 2012, advances in the field of Deep Learning have enabled many advances in this direction, particularly thanks to convolutional neura networks that have achieved near-human performance in many areas associated with visual perception (e.g. object recognition and detection, etc.). This thesis proposes an unsupervised approach to meet the needs of automatic visual inspection. This method, called AnoAEGAN, combines adversarial learning and the estimation of a probability density function. These two complementary approaches make it possible to jointly estimate the pixel-by-pixel probability of a visual defect on an image. The model is trained from a very limited number of images (i.e. less than 1000 images) without using expert knowledge to "label" the data beforehand. This method allows increased flexibility with a limited training time and therefore great versatility, demonstrated on ten different tasks without any modification of the model. This method should reduce development costs and the time required to deploy in production. This method can also be deployed in a complementary way to a supervised approach in order to benefit from the advantages of each approach
Книги з теми "Deep Unsupervised Learning"
Pal, Sujit, Amita Kapoor, Antonio Gulli, and François Chollet. Deep Learning with TensorFlow and Keras: Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models. Packt Publishing, Limited, 2022.
Знайти повний текст джерелаBonaccorso, Giuseppe. Hands-On Unsupervised Learning with Python: Implement Machine Learning and Deep Learning Models Using Scikit-Learn, TensorFlow, and More. Packt Publishing, Limited, 2019.
Знайти повний текст джерелаAdvanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, Deep RL, Unsupervised Learning, Object Detection and Segmentation, and More, 2nd Edition. Packt Publishing, Limited, 2020.
Знайти повний текст джерелаLeordeanu, Marius. Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision Using Graph-Based Techniques and Deep Neural Networks. Springer International Publishing AG, 2021.
Знайти повний текст джерелаLeordeanu, Marius. Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks. Springer, 2020.
Знайти повний текст джерелаЧастини книг з теми "Deep Unsupervised Learning"
Jo, Taeho. "Unsupervised Learning." In Deep Learning Foundations, 57–81. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32879-4_3.
Повний текст джерелаTanaka, Akinori, Akio Tomiya, and Koji Hashimoto. "Unsupervised Deep Learning." In Deep Learning and Physics, 103–26. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6108-9_6.
Повний текст джерелаVermeulen, Andreas François. "Unsupervised Learning: Deep Learning." In Industrial Machine Learning, 225–41. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5316-8_8.
Повний текст джерелаWani, M. Arif, Farooq Ahmad Bhat, Saduf Afzal, and Asif Iqbal Khan. "Unsupervised Deep Learning Architectures." In Studies in Big Data, 77–94. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6794-6_5.
Повний текст джерелаYe, Jong Chul. "Generative Models and Unsupervised Learning." In Geometry of Deep Learning, 267–313. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6046-7_13.
Повний текст джерелаLei, Chen. "Unsupervised Learning: Deep Generative Model." In Cognitive Intelligence and Robotics, 183–215. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2233-5_9.
Повний текст джерелаHu, Weihua, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama. "Unsupervised Discrete Representation Learning." In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 97–119. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28954-6_6.
Повний текст джерелаWani, M. Arif, Farooq Ahmad Bhat, Saduf Afzal, and Asif Iqbal Khan. "Unsupervised Deep Learning in Character Recognition." In Studies in Big Data, 133–49. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6794-6_8.
Повний текст джерелаZhang, Shufei, Kaizhu Huang, Rui Zhang, and Amir Hussain. "Improve Deep Learning with Unsupervised Objective." In Neural Information Processing, 720–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_74.
Повний текст джерелаTripathy, Sushreeta, and Muskaan Tabasum. "Autoencoder: An Unsupervised Deep Learning Approach." In Emerging Technologies in Data Mining and Information Security, 261–67. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4052-1_27.
Повний текст джерелаТези доповідей конференцій з теми "Deep Unsupervised Learning"
Maggu, Jyoti, and Angshul Majumdar. "Unsupervised Deep Transform Learning." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461498.
Повний текст джерелаLi, Changsheng, Handong Ma, Zhao Kang, Ye Yuan, Xiao-Yu Zhang, and Guoren Wang. "On Deep Unsupervised Active Learning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/364.
Повний текст джерелаChen, Junjie, William K. Cheung, and Anran Wang. "Learning Deep Unsupervised Binary Codes for Image Retrieval." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/85.
Повний текст джерелаXu, Yueyao. "Unsupervised Deep Learning for Text Steganalysis." In 2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI). IEEE, 2020. http://dx.doi.org/10.1109/iwecai50956.2020.00030.
Повний текст джерелаSiddique, A. B., Samet Oymak, and Vagelis Hristidis. "Unsupervised Paraphrasing via Deep Reinforcement Learning." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403231.
Повний текст джерелаSekmen, Ali, Ahmet Bugra Koku, Mustafa Parlaktuna, Ayad Abdul-Malek, and Nagendrababu Vanamala. "Unsupervised deep learning for subspace clustering." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258156.
Повний текст джерелаLiu, Yixin, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, and Shirui Pan. "Towards Unsupervised Deep Graph Structure Learning." In WWW '22: The ACM Web Conference 2022. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3485447.3512186.
Повний текст джерелаTang, Hao, and Kun Zhan. "Unsupervised confident co-promoting: refinery for pseudo labels on unsupervised person re-identification." In International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), edited by Sandeep Saxena. SPIE, 2022. http://dx.doi.org/10.1117/12.2640850.
Повний текст джерелаSumalvico, Maciej. "Unsupervised Learning of Morphology with Graph Sampling." In RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning. Incoma Ltd. Shoumen, Bulgaria, 2017. http://dx.doi.org/10.26615/978-954-452-049-6_093.
Повний текст джерелаZhan, Xiaohang, Jiahao Xie, Ziwei Liu, Yew-Soon Ong, and Chen Change Loy. "Online Deep Clustering for Unsupervised Representation Learning." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00672.
Повний текст джерелаЗвіти організацій з теми "Deep Unsupervised Learning"
Lin, Youzuo. Physics-guided Machine Learning: from Supervised Deep Networks to Unsupervised Lightweight Models. Office of Scientific and Technical Information (OSTI), August 2023. http://dx.doi.org/10.2172/1994110.
Повний текст джерелаTran, Anh, Theron Rodgers, and Timothy Wildey. Reification of latent microstructures: On supervised unsupervised and semi-supervised deep learning applications for microstructures in materials informatics. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1673174.
Повний текст джерелаMbani, Benson, Timm Schoening, and Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, May 2023. http://dx.doi.org/10.3289/sw_2_2023.
Повний текст джерела