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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.00865 |
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| _version_ | 1866913632620642304 |
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| author | Magal, Nicholas Tran, Minh Arakawa, Riku Nie, Suzanne |
| author_facet | Magal, Nicholas Tran, Minh Arakawa, Riku Nie, Suzanne |
| contents | This paper aims to document an effective way to improve multimodal co-learning by using aggressive modality dropout. We find that by using aggressive modality dropout we are able to reverse negative co-learning (NCL) to positive co-learning (PCL). Aggressive modality dropout can be used to "prep" a multimodal model for unimodal deployment, and dramatically increases model performance during negative co-learning, where during some experiments we saw a 20% gain in accuracy. We also benchmark our modality dropout technique against PCL to show that our modality drop out technique improves co-learning during PCL, although it does not have as much as an substantial effect as it does during NCL. Github: https://github.com/nmagal/modality_drop_for_colearning |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00865 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Negative to Positive Co-learning with Aggressive Modality Dropout Magal, Nicholas Tran, Minh Arakawa, Riku Nie, Suzanne Computation and Language Machine Learning This paper aims to document an effective way to improve multimodal co-learning by using aggressive modality dropout. We find that by using aggressive modality dropout we are able to reverse negative co-learning (NCL) to positive co-learning (PCL). Aggressive modality dropout can be used to "prep" a multimodal model for unimodal deployment, and dramatically increases model performance during negative co-learning, where during some experiments we saw a 20% gain in accuracy. We also benchmark our modality dropout technique against PCL to show that our modality drop out technique improves co-learning during PCL, although it does not have as much as an substantial effect as it does during NCL. Github: https://github.com/nmagal/modality_drop_for_colearning |
| title | Negative to Positive Co-learning with Aggressive Modality Dropout |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2501.00865 |