Zeitschriftenartikel zum Thema „Self-Supervised models“
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Anton, Jonah, Liam Castelli, Mun Fai Chan, Mathilde Outters, Wan Hee Tang, Venus Cheung, Pancham Shukla, Rahee Walambe und Ketan Kotecha. „How Well Do Self-Supervised Models Transfer to Medical Imaging?“ Journal of Imaging 8, Nr. 12 (01.12.2022): 320. http://dx.doi.org/10.3390/jimaging8120320.
Der volle Inhalt der QuelleGatopoulos, Ioannis, und Jakub M. Tomczak. „Self-Supervised Variational Auto-Encoders“. Entropy 23, Nr. 6 (14.06.2021): 747. http://dx.doi.org/10.3390/e23060747.
Der volle Inhalt der QuelleZhang, Ronghua, Yuanyuan Wang, Fangyuan Liu, Changzheng Liu, Yaping Song und Baohua Yu. „S2NMF: Information Self-Enhancement Self-Supervised Nonnegative Matrix Factorization for Recommendation“. Wireless Communications and Mobile Computing 2022 (30.08.2022): 1–10. http://dx.doi.org/10.1155/2022/4748858.
Der volle Inhalt der QuelleDang, Thanh-Vu, JinYoung Kim, Gwang-Hyun Yu, Ji Yong Kim, Young Hwan Park und ChilWoo Lee. „Korean Text to Gloss: Self-Supervised Learning approach“. Korean Institute of Smart Media 12, Nr. 1 (28.02.2023): 32–46. http://dx.doi.org/10.30693/smj.2023.12.1.32.
Der volle Inhalt der QuelleRisojević, V., und V. Stojnić. „DO WE STILL NEED IMAGENET PRE-TRAINING IN REMOTE SENSING SCENE CLASSIFICATION?“ International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (31.05.2022): 1399–406. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1399-2022.
Der volle Inhalt der QuelleImran, Abdullah-Al-Zubaer, Chao Huang, Hui Tang, Wei Fan, Yuan Xiao, Dingjun Hao, Zhen Qian und Demetri Terzopoulos. „Self-Supervised, Semi-Supervised, Multi-Context Learning for the Combined Classification and Segmentation of Medical Images (Student Abstract)“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 10 (03.04.2020): 13815–16. http://dx.doi.org/10.1609/aaai.v34i10.7179.
Der volle Inhalt der QuelleZhou, Meng, Zechen Li und Pengtao Xie. „Self-supervised Regularization for Text Classification“. Transactions of the Association for Computational Linguistics 9 (2021): 641–56. http://dx.doi.org/10.1162/tacl_a_00389.
Der volle Inhalt der QuelleGong, Yuan, Cheng-I. Lai, Yu-An Chung und James Glass. „SSAST: Self-Supervised Audio Spectrogram Transformer“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 10 (28.06.2022): 10699–709. http://dx.doi.org/10.1609/aaai.v36i10.21315.
Der volle Inhalt der QuelleChen, Xuehao, Jin Zhou, Yuehui Chen, Shiyuan Han, Yingxu Wang, Tao Du, Cheng Yang und Bowen Liu. „Self-Supervised Clustering Models Based on BYOL Network Structure“. Electronics 12, Nr. 23 (21.11.2023): 4723. http://dx.doi.org/10.3390/electronics12234723.
Der volle Inhalt der QuelleLuo, Dezhao, Chang Liu, Yu Zhou, Dongbao Yang, Can Ma, Qixiang Ye und Weiping Wang. „Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 07 (03.04.2020): 11701–8. http://dx.doi.org/10.1609/aaai.v34i07.6840.
Der volle Inhalt der QuelleTuncal, Kubra, Boran Sekeroglu und Rahib Abiyev. „Self-Supervised and Supervised Image Enhancement Networks with Time-Shift Module“. Electronics 13, Nr. 12 (13.06.2024): 2313. http://dx.doi.org/10.3390/electronics13122313.
Der volle Inhalt der QuelleKnoedler, Luzia, Chadi Salmi, Hai Zhu, Bruno Brito und Javier Alonso-Mora. „Improving Pedestrian Prediction Models With Self-Supervised Continual Learning“. IEEE Robotics and Automation Letters 7, Nr. 2 (April 2022): 4781–88. http://dx.doi.org/10.1109/lra.2022.3148475.
Der volle Inhalt der QuellePasad, Ankita, Chung-Ming Chien, Shane Settle und Karen Livescu. „What Do Self-Supervised Speech Models Know About Words?“ Transactions of the Association for Computational Linguistics 12 (2024): 372–91. http://dx.doi.org/10.1162/tacl_a_00656.
Der volle Inhalt der QuelleLi, Jingwei, Chi Zhang, Linyuan Wang, Penghui Ding, Lulu Hu, Bin Yan und Li Tong. „A Visual Encoding Model Based on Contrastive Self-Supervised Learning for Human Brain Activity along the Ventral Visual Stream“. Brain Sciences 11, Nr. 8 (29.07.2021): 1004. http://dx.doi.org/10.3390/brainsci11081004.
Der volle Inhalt der QuelleScheibenreif, L., M. Mommert und D. Borth. „CONTRASTIVE SELF-SUPERVISED DATA FUSION FOR SATELLITE IMAGERY“. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (17.05.2022): 705–11. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-705-2022.
Der volle Inhalt der QuelleYuan, Guotao, Hong Huang und Xin Li. „Self-supervised learning backdoor defense mixed with self-attention mechanism“. Journal of Computing and Electronic Information Management 12, Nr. 2 (30.03.2024): 81–88. http://dx.doi.org/10.54097/7hx9afkw.
Der volle Inhalt der QuelleZhang, Ye. „Application of self-supervised learning in natural language processing“. Journal of Computing and Electronic Information Management 12, Nr. 1 (28.02.2024): 23–26. http://dx.doi.org/10.54097/urpv6i8g3j.
Der volle Inhalt der QuelleDominic, Jeffrey, Nandita Bhaskhar, Arjun D. Desai, Andrew Schmidt, Elka Rubin, Beliz Gunel, Garry E. Gold et al. „Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised Learning“. Bioengineering 10, Nr. 2 (04.02.2023): 207. http://dx.doi.org/10.3390/bioengineering10020207.
Der volle Inhalt der QuelleZeng, Jiaqi, und Pengtao Xie. „Contrastive Self-supervised Learning for Graph Classification“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 12 (18.05.2021): 10824–32. http://dx.doi.org/10.1609/aaai.v35i12.17293.
Der volle Inhalt der QuelleWagner, Royden, Carlos Fernandez Lopez und Christoph Stiller. „Self-supervised pseudo-colorizing of masked cells“. PLOS ONE 18, Nr. 8 (24.08.2023): e0290561. http://dx.doi.org/10.1371/journal.pone.0290561.
Der volle Inhalt der QuelleLiu, Yuanyuan, und Qianqian Liu. „Research on Self-Supervised Comparative Learning for Computer Vision“. Journal of Electronic Research and Application 5, Nr. 3 (17.08.2021): 5–17. http://dx.doi.org/10.26689/jera.v5i3.2320.
Der volle Inhalt der QuelleEsser, Pascal, Maximilian Fleissner und Debarghya Ghoshdastidar. „Non-parametric Representation Learning with Kernels“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 11 (24.03.2024): 11910–18. http://dx.doi.org/10.1609/aaai.v38i11.29077.
Der volle Inhalt der QuellePolceanu, Mihai, Julie Porteous, Alan Lindsay und Marc Cavazza. „Narrative Plan Generation with Self-Supervised Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 7 (18.05.2021): 5984–92. http://dx.doi.org/10.1609/aaai.v35i7.16747.
Der volle Inhalt der QuelleTóth, Martos, und Nelson Sommerfeldt. „PV self-consumption prediction methods using supervised machine learning“. E3S Web of Conferences 362 (2022): 02003. http://dx.doi.org/10.1051/e3sconf/202236202003.
Der volle Inhalt der QuelleMustapha, Ahmad, Wael Khreich und Wes Masri. „Inter-model interpretability: Self-supervised models as a case study“. Array 22 (Juli 2024): 100350. http://dx.doi.org/10.1016/j.array.2024.100350.
Der volle Inhalt der QuelleShi, Haizhou, Youcai Zhang, Siliang Tang, Wenjie Zhu, Yaqian Li, Yandong Guo und Yueting Zhuang. „On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 2 (28.06.2022): 2225–34. http://dx.doi.org/10.1609/aaai.v36i2.20120.
Der volle Inhalt der QuelleMakarov, Ilya, Maria Bakhanova, Sergey Nikolenko und Olga Gerasimova. „Self-supervised recurrent depth estimation with attention mechanisms“. PeerJ Computer Science 8 (31.01.2022): e865. http://dx.doi.org/10.7717/peerj-cs.865.
Der volle Inhalt der QuelleHu, Fanghuai, Zhiqing Shao und Tong Ruan. „Self-Supervised Chinese Ontology Learning from Online Encyclopedias“. Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/848631.
Der volle Inhalt der QuelleShwartz Ziv, Ravid, und Yann LeCun. „To Compress or Not to Compress—Self-Supervised Learning and Information Theory: A Review“. Entropy 26, Nr. 3 (12.03.2024): 252. http://dx.doi.org/10.3390/e26030252.
Der volle Inhalt der QuelleMontero Quispe, Kevin G., Daniel M. S. Utyiama, Eulanda M. dos Santos, Horácio A. B. F. Oliveira und Eduardo J. P. Souto. „Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals“. Sensors 22, Nr. 23 (23.11.2022): 9102. http://dx.doi.org/10.3390/s22239102.
Der volle Inhalt der QuelleLivieris, Ioannis, Andreas Kanavos, Vassilis Tampakas und Panagiotis Pintelas. „An Auto-Adjustable Semi-Supervised Self-Training Algorithm“. Algorithms 11, Nr. 9 (14.09.2018): 139. http://dx.doi.org/10.3390/a11090139.
Der volle Inhalt der QuelleKahatapitiya, Kumara, Zhou Ren, Haoxiang Li, Zhenyu Wu, Michael S. Ryoo und Gang Hua. „Weakly-Guided Self-Supervised Pretraining for Temporal Activity Detection“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 1 (26.06.2023): 1078–86. http://dx.doi.org/10.1609/aaai.v37i1.25189.
Der volle Inhalt der QuelleCheng, Jiashun, Man Li, Jia Li und Fugee Tsung. „Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 6 (26.06.2023): 7131–39. http://dx.doi.org/10.1609/aaai.v37i6.25870.
Der volle Inhalt der QuelleFedden, Leon, Zhenning Zhang, Khan Baykaner, Qin Li und Lucas Bordeaux. „Abstract 1937: DIME-CT: Self-supervised learning for medical image analysis using patch-based embeddings“. Cancer Research 82, Nr. 12_Supplement (15.06.2022): 1937. http://dx.doi.org/10.1158/1538-7445.am2022-1937.
Der volle Inhalt der QuelleXu, Xiangdong, Krzysztof Przystupa und Orest Kochan. „Social Recommendation Algorithm Based on Self-Supervised Hypergraph Attention“. Electronics 12, Nr. 4 (10.02.2023): 906. http://dx.doi.org/10.3390/electronics12040906.
Der volle Inhalt der QuelleManessi, Franco, und Alessandro Rozza. „Graph-based neural network models with multiple self-supervised auxiliary tasks“. Pattern Recognition Letters 148 (August 2021): 15–21. http://dx.doi.org/10.1016/j.patrec.2021.04.021.
Der volle Inhalt der QuelleZhang, Jian, Jianing Yang, Jun Yu und Jianping Fan. „Semisupervised image classification by mutual learning of multiple self‐supervised models“. International Journal of Intelligent Systems 37, Nr. 5 (14.01.2022): 3117–41. http://dx.doi.org/10.1002/int.22814.
Der volle Inhalt der QuelleLiu, Gang, Silu He, Xing Han, Qinyao Luo, Ronghua Du, Xinsha Fu und Ling Zhao. „Self-Supervised Spatiotemporal Masking Strategy-Based Models for Traffic Flow Forecasting“. Symmetry 15, Nr. 11 (31.10.2023): 2002. http://dx.doi.org/10.3390/sym15112002.
Der volle Inhalt der QuelleJoshi, Amey, Hrishitaa Kurchania und Harikrishnan R. „Robust Object Segmentation using 3D Mesh Models and Self-Supervised Learning“. Procedia Computer Science 235 (2024): 907–15. http://dx.doi.org/10.1016/j.procs.2024.04.086.
Der volle Inhalt der QuelleGao, Min, Yingmei Wei, Yuxiang Xie und Yitong Zhang. „Traffic Prediction with Self-Supervised Learning: A Heterogeneity-Aware Model for Urban Traffic Flow Prediction Based on Self-Supervised Learning“. Mathematics 12, Nr. 9 (24.04.2024): 1290. http://dx.doi.org/10.3390/math12091290.
Der volle Inhalt der QuelleXu, Yanjie, Hao Sun, Jin Chen, Lin Lei, Kefeng Ji und Gangyao Kuang. „Adversarial Self-Supervised Learning for Robust SAR Target Recognition“. Remote Sensing 13, Nr. 20 (17.10.2021): 4158. http://dx.doi.org/10.3390/rs13204158.
Der volle Inhalt der QuelleJaved, Tahir, Kaushal Bhogale, Abhigyan Raman, Pratyush Kumar, Anoop Kunchukuttan und Mitesh M. Khapra. „IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian Languages“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 11 (26.06.2023): 12942–50. http://dx.doi.org/10.1609/aaai.v37i11.26521.
Der volle Inhalt der QuelleZhao, Nanxuan, Zhirong Wu, Rynson W. H. Lau und Stephen Lin. „Distilling Localization for Self-Supervised Representation Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 12 (18.05.2021): 10990–98. http://dx.doi.org/10.1609/aaai.v35i12.17312.
Der volle Inhalt der QuelleGuo, Yuzhi, Jiaxiang Wu, Hehuan Ma und Junzhou Huang. „Self-Supervised Pre-training for Protein Embeddings Using Tertiary Structures“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 6 (28.06.2022): 6801–9. http://dx.doi.org/10.1609/aaai.v36i6.20636.
Der volle Inhalt der QuelleLin, Ken, Xiongwen Quan, Wenya Yin und Han Zhang. „A Contrastive Learning Pre-Training Method for Motif Occupancy Identification“. International Journal of Molecular Sciences 23, Nr. 9 (24.04.2022): 4699. http://dx.doi.org/10.3390/ijms23094699.
Der volle Inhalt der QuelleNimitsurachat, Peranut, und Peter Washington. „Audio-Based Emotion Recognition Using Self-Supervised Learning on an Engineered Feature Space“. AI 5, Nr. 1 (17.01.2024): 195–207. http://dx.doi.org/10.3390/ai5010011.
Der volle Inhalt der QuelleParmar, Chaitanya, Albert Juan Ramon, Nicole L. Stone, Spyros Triantos, Joel Greshock und Kristopher Standish. „Generalizable FGFR prediction across tumor types using self supervised learning.“ Journal of Clinical Oncology 41, Nr. 16_suppl (01.06.2023): e15057-e15057. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e15057.
Der volle Inhalt der QuelleDíaz, Gabriel, Billy Peralta, Luis Caro und Orietta Nicolis. „Co-Training for Visual Object Recognition Based on Self-Supervised Models Using a Cross-Entropy Regularization“. Entropy 23, Nr. 4 (01.04.2021): 423. http://dx.doi.org/10.3390/e23040423.
Der volle Inhalt der QuelleChoudhary, Nurendra, Charu C. Aggarwal, Karthik Subbian und Chandan K. Reddy. „Self-supervised Short-text Modeling through Auxiliary Context Generation“. ACM Transactions on Intelligent Systems and Technology 13, Nr. 3 (30.06.2022): 1–21. http://dx.doi.org/10.1145/3511712.
Der volle Inhalt der QuelleZhang, Ming, Xin Gu, Ji Qi, Zhenshi Zhang, Hemeng Yang, Jun Xu, Chengli Peng und Haifeng Li. „CDEST: Class Distinguishability-Enhanced Self-Training Method for Adopting Pre-Trained Models to Downstream Remote Sensing Image Semantic Segmentation“. Remote Sensing 16, Nr. 7 (06.04.2024): 1293. http://dx.doi.org/10.3390/rs16071293.
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