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Building robust multimodal models are crucial for achieving reliable deployment in the wild.
Adversarial robustness may be at odds with simplicity
Preetum Nakkiran. 2019 · 1901
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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 · 1993
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Amir Zadeh, Rowan Zellers, Eli Pincus, and Louis-Philippe Morency. 2016 · 2016
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Dong Su, Huan Zhang, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, and Yupeng Gao. 2018 · 2018
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Yao-Hung Hubert Tsai, Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency, and Ruslan Salakhutdinov. 2018 · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
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Robustness may be at odds with accuracy
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Improving multimodal fusion with hierarchical mutual information maximization for multimodal sentiment analysis
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Smil: Multimodal learning with severely missing modality
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A text-centered shared-private framework via cross-modal prediction for multimodal sentiment analysis
Yang Wu, Zijie Lin, Yanyan Zhao, Bing Qin, and Li-Nan Zhu. 2021 · 2021
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Learning modality-specific representations with self-supervised multi-task learning for multimodal sentiment analysis
Wenmeng Yu, Hua Xu, Ziqi Yuan, and Jiele Wu. 2021 · 2021
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