LANISTR: Multimodal Learning from Structured and Unstructured Data
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| Format: | Preprint |
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2023
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| _version_ | 1866916220002893824 |
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| author | Ebrahimi, Sayna Arik, Sercan O. Dong, Yihe Pfister, Tomas |
| author_facet | Ebrahimi, Sayna Arik, Sercan O. Dong, Yihe Pfister, Tomas |
| contents | Multimodal large-scale pretraining has shown impressive performance for unstructured data such as language and image. However, a prevalent real-world scenario involves structured data types, tabular and time-series, along with unstructured data. Such scenarios have been understudied. To bridge this gap, we propose LANISTR, an attention-based framework to learn from LANguage, Image, and STRuctured data. The core of LANISTR's methodology is rooted in \textit{masking-based} training applied across both unimodal and multimodal levels. In particular, we introduce a new similarity-based multimodal masking loss that enables it to learn cross-modal relations from large-scale multimodal data with missing modalities. On two real-world datasets, MIMIC-IV (from healthcare) and Amazon Product Review (from retail), LANISTR demonstrates remarkable improvements, 6.6\% (in AUROC) and 14\% (in accuracy) when fine-tuned with 0.1\% and 0.01\% of labeled data, respectively, compared to the state-of-the-art alternatives. Notably, these improvements are observed even with very high ratio of samples (35.7\% and 99.8\% respectively) not containing all modalities, underlining the robustness of LANISTR to practical missing modality challenge. Our code and models will be available at https://github.com/google-research/lanistr |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_16556 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LANISTR: Multimodal Learning from Structured and Unstructured Data Ebrahimi, Sayna Arik, Sercan O. Dong, Yihe Pfister, Tomas Machine Learning Artificial Intelligence Multimodal large-scale pretraining has shown impressive performance for unstructured data such as language and image. However, a prevalent real-world scenario involves structured data types, tabular and time-series, along with unstructured data. Such scenarios have been understudied. To bridge this gap, we propose LANISTR, an attention-based framework to learn from LANguage, Image, and STRuctured data. The core of LANISTR's methodology is rooted in \textit{masking-based} training applied across both unimodal and multimodal levels. In particular, we introduce a new similarity-based multimodal masking loss that enables it to learn cross-modal relations from large-scale multimodal data with missing modalities. On two real-world datasets, MIMIC-IV (from healthcare) and Amazon Product Review (from retail), LANISTR demonstrates remarkable improvements, 6.6\% (in AUROC) and 14\% (in accuracy) when fine-tuned with 0.1\% and 0.01\% of labeled data, respectively, compared to the state-of-the-art alternatives. Notably, these improvements are observed even with very high ratio of samples (35.7\% and 99.8\% respectively) not containing all modalities, underlining the robustness of LANISTR to practical missing modality challenge. Our code and models will be available at https://github.com/google-research/lanistr |
| title | LANISTR: Multimodal Learning from Structured and Unstructured Data |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2305.16556 |