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The popularity of multimodal sensors and the accessibility of the Internet have brought us a massive amount of unlabeled multimodal data.
Combining labeled and unlabeled data with co-training
Avrim Blum and Tom Mitchell · 1998
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Naive (bayes) at forty: The independence assumption in information retrieval
David D Lewis · 1998
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Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
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Very deep convolutional networks for large-scale image recognition
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Soundnet: Learning sound representations from unlabeled video
Yusuf Aytar, Carl Vondrick, and Antonio Torralba · 2016
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Cross modal distillation for supervision transfer
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Cross-modal adaptation for rgb-d detection
Judy Hoffman, Saurabh Gupta, Jian Leong, Sergio Guadarrama, and Trevor Darrell · 2016
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Ambient sound provides supervision for visual learning
Andrew Owens, Jiajun Wu, Josh H McDermott, William T Freeman, and Antonio Torralba · 2016
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Multimodal emotion recognition using deep learning architectures
Hiranmayi Ranganathan, Shayok Chakraborty, and Sethuraman Panchanathan · 2016
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AUTOMATIC SPEECH RECOGNITION
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Look, listen and learn
Relja Arandjelovic and Andrew Zisserman · 2017
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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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Audio set: An ontology and human-labeled dataset for audio events
Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter · 2017
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Cnn architectures for large-scale audio classification
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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End-to-end multimodal emotion recognition using deep neural networks
Panagiotis Tzirakis, George Trigeorgis, Mihalis A Nicolaou, Björn W Schuller, and Stefanos Zafeiriou · 2017
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Tensor fusion network for multimodal sentiment analysis
Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency · 2017
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Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring
David Berthelot, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel · 2020
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Vggsound: A large-scale audio-visual dataset
Honglie Chen, Weidi Xie, Andrea Vedaldi, and Andrew Zisserman · 2020
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Naive-student: Leveraging semi-supervised learning in video sequences for urban scene segmentation
Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng, Maxwell D Collins, Ekin D Cubuk, Barret Zoph, Hartwig Adam, and Jonathon Shlens · 2020
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Bi-directional cross-modality feature propagation with separation-and-aggregation gate for rgb-d semantic segmentation
Xiaokang Chen, Kwan-Yee Lin, Jingbo Wang, Wayne Wu, Chen Qian, Hongsheng Li, and Gang Zeng · 2020
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Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges
Di Feng, Christian Haase-Schuetz, Lars Rosenbaum, Heinz Hertlein, Claudius Glaeser, Fabian Timm, Werner Wiesbeck, and Klaus Dietmayer · 2020
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The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english
Steven R Livingstone and Frank A Russo · 2018
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Multimodal sentiment analysis using hierarchical fusion with context modeling
Navonil Majumder, Devamanyu Hazarika, Alexander Gelbukh, Erik Cambria, and Soujanya Poria · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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Through-wall human pose estimation using radio signals
Mingmin Zhao, Tianhong Li, Mohammad Abu Alsheikh, Yonglong Tian, Hang Zhao, Antonio Torralba, and Dina Katabi · 2018
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
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Similarity-preserving knowledge distillation
Frederick Tung and Greg Mori · 2019
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Self-supervised model adaptation for multimodal semantic segmentation
Abhinav Valada, Rohit Mohan, and Wolfram Burgard · 2019
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Understanding self-training for gradual domain adaptation
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Hieu Pham, Qizhe Xie, Zihang Dai, and Quoc V Le · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
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Tcgm: An information-theoretic framework for semi-supervised multi-modality learning
Xinwei Sun, Yilun Xu, Peng Cao, Yuqing Kong, Lingjing Hu, Shanghang Zhang, and Yizhou Wang · 2020
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Deep multimodal fusion by channel exchanging
Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong, and Junzhou Huang · 2020
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Theoretical analysis of self-training with deep networks on unlabeled data
Colin Wei, Kendrick Shen, Yining Chen, and Tengyu Ma · 2020
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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Knowledge as priors: Cross-modal knowledge generalization for datasets without superior knowledge
Long Zhao et al · 2020
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Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, and Quoc Le · 2020
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What makes multimodal learning better than single (provably)
Yu Huang, Chenzhuang Du, Zihui Xue, Xuanyao Chen, Hang Zhao, and Longbo Huang · 2021
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Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks
Lin Wang and Kuk-Jin Yoon · 2021
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