Research map / Vision-language and multimodal / Cross-modal learning
An Efficient Approach to Informative Feature Extraction from Multimodal Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add supervised label loss to Soft-HGR | Soft-HGR on XRMB · XRMB phonetic prediction, 10% labels | accuracy: 72.2% -> 76.3% | Helped |
| Add supervised label loss to Soft-HGR | Soft-HGR on XRMB · XRMB phonetic prediction, 50% labels | accuracy: 81.2% -> 85.0% | Helped |
| Add supervised label loss to Soft-HGR | Soft-HGR on XRMB · XRMB phonetic prediction, 100% labels | accuracy: 86.4% -> 88.0% | Helped |
| Add the Soft-HGR objective to the predictive model (ablation) | Semi Soft-HGR on KKBox · KKBox, no missing labels or modalities | AUC | Helped |
| Use unsupervised Soft-HGR alongside supervised training | Semi Soft-HGR on KKBox · KKBox, no missing labels or modalities | AUC: 0.6803 -> 0.6972 | Helped |
| Use multimodal semi-supervised Soft-HGR with incomplete modalities | Semi Soft-HGR on KKBox · KKBox, 50% missing modalities | AUC: 0.6574 -> 0.6682 | Helped |
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