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Related Questions
- What are the key differences between unimodal and multimodal learning models, and how do they impact the complexity of the model development process?
- How can multimodal learning models be trained with large datasets that contain diverse and heterogeneous data types?
- What are some common architectures used in multimodal learning models, and how do they handle the fusion of different modalities?
- How can multimodal learning models handle the problem of modality shift, where the distribution of the training data differs from the distribution of the test data?
- What are some techniques used to evaluate the performance of multimodal learning models, and how can they be compared to unimodal models?
- How can multimodal learning models be applied to real-world applications such as image-text classification, speech recognition, and human-computer interaction?
- What are some challenges in deploying multimodal learning models in real-world settings, and how can they be addressed with appropriate explainability and interpretability techniques?
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