NCA-GENM Question 109
Select 3You are working on a multimodal Generative AI project that integrates text, images, and audio data. During preprocessing, you notice missing text descriptions for some images and low-quality audio recordings in another subset of the data. Which approach(es) should you consider to address these challenges while maintaining the integrity of the multimodal dataset?
- A
Use imputation techniques to generate synthetic text descriptions for the images with missing data.
- B
Discard all data samples that have any missing modality to ensure uniformity across the dataset.
- C
Apply data augmentation techniques to enhance the quality of the audio recordings.
- D
Use a multimodal model capable of handling missing modalities during training without discarding incomplete data.
- E
Replace missing modalities with random noise to balance the dataset.
Show answer and explanation
Correct answers: A, C, D
Explanation
In multimodal datasets, addressing missing or incomplete data is critical to maintaining data integrity and model performance. Strategies like imputation for missing data, data augmentation for quality enhancement, and using models designed to handle missing modalities are all effective approaches. Discarding data or introducing random noise, on the other hand, can degrade the dataset's quality or the model's performance.
- A. Correct.
Using imputation techniques to generate synthetic text descriptions can help fill in the missing data while preserving the dataset's completeness, making it a viable solution.
- B. Incorrect.
Discarding all data samples with missing modalities would result in a significant loss of potentially valuable data and is generally not recommended.
- C. Correct.
Data augmentation techniques can improve the quality or diversity of the audio recordings, enhancing the dataset and the model's ability to generalize.
- D. Correct.
Using a multimodal model capable of handling missing modalities is a robust approach, as it allows the model to learn from incomplete data without discarding it.
- E. Incorrect.
Replacing missing modalities with random noise could negatively impact model training and is not an effective strategy for handling missing data.