Journal articles on the topic '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, and Ketan Kotecha. "How Well Do Self-Supervised Models Transfer to Medical Imaging?" Journal of Imaging 8, no. 12 (December 1, 2022): 320. http://dx.doi.org/10.3390/jimaging8120320.
Gatopoulos, Ioannis, and Jakub M. Tomczak. "Self-Supervised Variational Auto-Encoders." Entropy 23, no. 6 (June 14, 2021): 747. http://dx.doi.org/10.3390/e23060747.
Zhang, Ronghua, Yuanyuan Wang, Fangyuan Liu, Changzheng Liu, Yaping Song, and Baohua Yu. "S2NMF: Information Self-Enhancement Self-Supervised Nonnegative Matrix Factorization for Recommendation." Wireless Communications and Mobile Computing 2022 (August 30, 2022): 1–10. http://dx.doi.org/10.1155/2022/4748858.
Dang, Thanh-Vu, JinYoung Kim, Gwang-Hyun Yu, Ji Yong Kim, Young Hwan Park, and ChilWoo Lee. "Korean Text to Gloss: Self-Supervised Learning approach." Korean Institute of Smart Media 12, no. 1 (February 28, 2023): 32–46. http://dx.doi.org/10.30693/smj.2023.12.1.32.
Risojević, V., and 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 (May 31, 2022): 1399–406. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1399-2022.
Imran, Abdullah-Al-Zubaer, Chao Huang, Hui Tang, Wei Fan, Yuan Xiao, Dingjun Hao, Zhen Qian, and 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, no. 10 (April 3, 2020): 13815–16. http://dx.doi.org/10.1609/aaai.v34i10.7179.
Zhou, Meng, Zechen Li, and 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.
Gong, Yuan, Cheng-I. Lai, Yu-An Chung, and James Glass. "SSAST: Self-Supervised Audio Spectrogram Transformer." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10699–709. http://dx.doi.org/10.1609/aaai.v36i10.21315.
Chen, Xuehao, Jin Zhou, Yuehui Chen, Shiyuan Han, Yingxu Wang, Tao Du, Cheng Yang, and Bowen Liu. "Self-Supervised Clustering Models Based on BYOL Network Structure." Electronics 12, no. 23 (November 21, 2023): 4723. http://dx.doi.org/10.3390/electronics12234723.
Luo, Dezhao, Chang Liu, Yu Zhou, Dongbao Yang, Can Ma, Qixiang Ye, and Weiping Wang. "Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11701–8. http://dx.doi.org/10.1609/aaai.v34i07.6840.
Tuncal, Kubra, Boran Sekeroglu, and Rahib Abiyev. "Self-Supervised and Supervised Image Enhancement Networks with Time-Shift Module." Electronics 13, no. 12 (June 13, 2024): 2313. http://dx.doi.org/10.3390/electronics13122313.
Knoedler, Luzia, Chadi Salmi, Hai Zhu, Bruno Brito, and Javier Alonso-Mora. "Improving Pedestrian Prediction Models With Self-Supervised Continual Learning." IEEE Robotics and Automation Letters 7, no. 2 (April 2022): 4781–88. http://dx.doi.org/10.1109/lra.2022.3148475.
Pasad, Ankita, Chung-Ming Chien, Shane Settle, and 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.
Li, Jingwei, Chi Zhang, Linyuan Wang, Penghui Ding, Lulu Hu, Bin Yan, and Li Tong. "A Visual Encoding Model Based on Contrastive Self-Supervised Learning for Human Brain Activity along the Ventral Visual Stream." Brain Sciences 11, no. 8 (July 29, 2021): 1004. http://dx.doi.org/10.3390/brainsci11081004.
Scheibenreif, L., M. Mommert, and D. Borth. "CONTRASTIVE SELF-SUPERVISED DATA FUSION FOR SATELLITE IMAGERY." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 705–11. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-705-2022.
Yuan, Guotao, Hong Huang, and Xin Li. "Self-supervised learning backdoor defense mixed with self-attention mechanism." Journal of Computing and Electronic Information Management 12, no. 2 (March 30, 2024): 81–88. http://dx.doi.org/10.54097/7hx9afkw.
Zhang, Ye. "Application of self-supervised learning in natural language processing." Journal of Computing and Electronic Information Management 12, no. 1 (February 28, 2024): 23–26. http://dx.doi.org/10.54097/urpv6i8g3j.
Dominic, 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, no. 2 (February 4, 2023): 207. http://dx.doi.org/10.3390/bioengineering10020207.
Zeng, Jiaqi, and Pengtao Xie. "Contrastive Self-supervised Learning for Graph Classification." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10824–32. http://dx.doi.org/10.1609/aaai.v35i12.17293.
Wagner, Royden, Carlos Fernandez Lopez, and Christoph Stiller. "Self-supervised pseudo-colorizing of masked cells." PLOS ONE 18, no. 8 (August 24, 2023): e0290561. http://dx.doi.org/10.1371/journal.pone.0290561.
Liu, Yuanyuan, and Qianqian Liu. "Research on Self-Supervised Comparative Learning for Computer Vision." Journal of Electronic Research and Application 5, no. 3 (August 17, 2021): 5–17. http://dx.doi.org/10.26689/jera.v5i3.2320.
Esser, Pascal, Maximilian Fleissner, and Debarghya Ghoshdastidar. "Non-parametric Representation Learning with Kernels." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (March 24, 2024): 11910–18. http://dx.doi.org/10.1609/aaai.v38i11.29077.
Polceanu, Mihai, Julie Porteous, Alan Lindsay, and Marc Cavazza. "Narrative Plan Generation with Self-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (May 18, 2021): 5984–92. http://dx.doi.org/10.1609/aaai.v35i7.16747.
Tóth, Martos, and 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.
Mustapha, Ahmad, Wael Khreich, and Wes Masri. "Inter-model interpretability: Self-supervised models as a case study." Array 22 (July 2024): 100350. http://dx.doi.org/10.1016/j.array.2024.100350.
Shi, Haizhou, Youcai Zhang, Siliang Tang, Wenjie Zhu, Yaqian Li, Yandong Guo, and Yueting Zhuang. "On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 2225–34. http://dx.doi.org/10.1609/aaai.v36i2.20120.
Makarov, Ilya, Maria Bakhanova, Sergey Nikolenko, and Olga Gerasimova. "Self-supervised recurrent depth estimation with attention mechanisms." PeerJ Computer Science 8 (January 31, 2022): e865. http://dx.doi.org/10.7717/peerj-cs.865.
Hu, Fanghuai, Zhiqing Shao, and 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.
Shwartz Ziv, Ravid, and Yann LeCun. "To Compress or Not to Compress—Self-Supervised Learning and Information Theory: A Review." Entropy 26, no. 3 (March 12, 2024): 252. http://dx.doi.org/10.3390/e26030252.
Montero Quispe, Kevin G., Daniel M. S. Utyiama, Eulanda M. dos Santos, Horácio A. B. F. Oliveira, and Eduardo J. P. Souto. "Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals." Sensors 22, no. 23 (November 23, 2022): 9102. http://dx.doi.org/10.3390/s22239102.
Livieris, Ioannis, Andreas Kanavos, Vassilis Tampakas, and Panagiotis Pintelas. "An Auto-Adjustable Semi-Supervised Self-Training Algorithm." Algorithms 11, no. 9 (September 14, 2018): 139. http://dx.doi.org/10.3390/a11090139.
Kahatapitiya, Kumara, Zhou Ren, Haoxiang Li, Zhenyu Wu, Michael S. Ryoo, and Gang Hua. "Weakly-Guided Self-Supervised Pretraining for Temporal Activity Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (June 26, 2023): 1078–86. http://dx.doi.org/10.1609/aaai.v37i1.25189.
Cheng, Jiashun, Man Li, Jia Li, and Fugee Tsung. "Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 7131–39. http://dx.doi.org/10.1609/aaai.v37i6.25870.
Fedden, Leon, Zhenning Zhang, Khan Baykaner, Qin Li, and Lucas Bordeaux. "Abstract 1937: DIME-CT: Self-supervised learning for medical image analysis using patch-based embeddings." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1937. http://dx.doi.org/10.1158/1538-7445.am2022-1937.
Xu, Xiangdong, Krzysztof Przystupa, and Orest Kochan. "Social Recommendation Algorithm Based on Self-Supervised Hypergraph Attention." Electronics 12, no. 4 (February 10, 2023): 906. http://dx.doi.org/10.3390/electronics12040906.
Manessi, Franco, and 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.
Zhang, Jian, Jianing Yang, Jun Yu, and Jianping Fan. "Semisupervised image classification by mutual learning of multiple self‐supervised models." International Journal of Intelligent Systems 37, no. 5 (January 14, 2022): 3117–41. http://dx.doi.org/10.1002/int.22814.
Liu, Gang, Silu He, Xing Han, Qinyao Luo, Ronghua Du, Xinsha Fu, and Ling Zhao. "Self-Supervised Spatiotemporal Masking Strategy-Based Models for Traffic Flow Forecasting." Symmetry 15, no. 11 (October 31, 2023): 2002. http://dx.doi.org/10.3390/sym15112002.
Joshi, Amey, Hrishitaa Kurchania, and 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.
Gao, Min, Yingmei Wei, Yuxiang Xie, and Yitong Zhang. "Traffic Prediction with Self-Supervised Learning: A Heterogeneity-Aware Model for Urban Traffic Flow Prediction Based on Self-Supervised Learning." Mathematics 12, no. 9 (April 24, 2024): 1290. http://dx.doi.org/10.3390/math12091290.
Xu, Yanjie, Hao Sun, Jin Chen, Lin Lei, Kefeng Ji, and Gangyao Kuang. "Adversarial Self-Supervised Learning for Robust SAR Target Recognition." Remote Sensing 13, no. 20 (October 17, 2021): 4158. http://dx.doi.org/10.3390/rs13204158.
Javed, Tahir, Kaushal Bhogale, Abhigyan Raman, Pratyush Kumar, Anoop Kunchukuttan, and Mitesh M. Khapra. "IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian Languages." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (June 26, 2023): 12942–50. http://dx.doi.org/10.1609/aaai.v37i11.26521.
Zhao, Nanxuan, Zhirong Wu, Rynson W. H. Lau, and Stephen Lin. "Distilling Localization for Self-Supervised Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10990–98. http://dx.doi.org/10.1609/aaai.v35i12.17312.
Guo, Yuzhi, Jiaxiang Wu, Hehuan Ma, and Junzhou Huang. "Self-Supervised Pre-training for Protein Embeddings Using Tertiary Structures." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6801–9. http://dx.doi.org/10.1609/aaai.v36i6.20636.
Lin, Ken, Xiongwen Quan, Wenya Yin, and Han Zhang. "A Contrastive Learning Pre-Training Method for Motif Occupancy Identification." International Journal of Molecular Sciences 23, no. 9 (April 24, 2022): 4699. http://dx.doi.org/10.3390/ijms23094699.
Nimitsurachat, Peranut, and Peter Washington. "Audio-Based Emotion Recognition Using Self-Supervised Learning on an Engineered Feature Space." AI 5, no. 1 (January 17, 2024): 195–207. http://dx.doi.org/10.3390/ai5010011.
Parmar, Chaitanya, Albert Juan Ramon, Nicole L. Stone, Spyros Triantos, Joel Greshock, and Kristopher Standish. "Generalizable FGFR prediction across tumor types using self supervised learning." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): e15057-e15057. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e15057.
Díaz, Gabriel, Billy Peralta, Luis Caro, and Orietta Nicolis. "Co-Training for Visual Object Recognition Based on Self-Supervised Models Using a Cross-Entropy Regularization." Entropy 23, no. 4 (April 1, 2021): 423. http://dx.doi.org/10.3390/e23040423.
Choudhary, Nurendra, Charu C. Aggarwal, Karthik Subbian, and Chandan K. Reddy. "Self-supervised Short-text Modeling through Auxiliary Context Generation." ACM Transactions on Intelligent Systems and Technology 13, no. 3 (June 30, 2022): 1–21. http://dx.doi.org/10.1145/3511712.
Zhang, Ming, Xin Gu, Ji Qi, Zhenshi Zhang, Hemeng Yang, Jun Xu, Chengli Peng, and Haifeng Li. "CDEST: Class Distinguishability-Enhanced Self-Training Method for Adopting Pre-Trained Models to Downstream Remote Sensing Image Semantic Segmentation." Remote Sensing 16, no. 7 (April 6, 2024): 1293. http://dx.doi.org/10.3390/rs16071293.