Summary of Triple Disentangled Representation Learning For Multimodal Affective Analysis, by Ying Zhou et al.
Triple Disentangled Representation Learning for Multimodal Affective Analysis
by Ying Zhou, Xuefeng Liang, Han Chen, Yin Zhao, Xin Chen, Lida Yu
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed TriDiRA approach offers a novel solution for multimodal learning by disentangling modality-invariant, effective modality-specific, and ineffective modality-specific representations from input data. This method fuses only the modality-invariant and effective modality-specific representations to alleviate the impact of irrelevant information across modalities during model training. The authors demonstrate the effectiveness and generalization of their approach through extensive experiments on four benchmark datasets, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TriDiRA is a new way to learn from multiple types of data, like images, sounds, and text. Right now, this type of learning is very good at understanding emotions and feelings. But sometimes the different types of data can have information that isn’t helpful or even gets in the way. This new approach helps remove that extra information so the models are better at their jobs. |
Keywords
* Artificial intelligence * Generalization
