NCA-GENM Question 3
Single answerA team is building a generative AI model that can create captions for images. They are debating which machine learning technique to use. Which technique is most suitable for this scenario and why?
- A
Supervised Learning because labeled image-caption pairs can help the model learn the relationship between images and their textual descriptions.
- B
Unsupervised Learning because the model should automatically find patterns in images without needing labeled data.
- C
Reinforcement Learning because it allows the model to optimize captions by interacting with users and receiving feedback.
- D
Semi-Supervised Learning because it can leverage a small amount of labeled data and a large amount of unlabeled data to generate captions.
Show answer and explanation
Correct answer: A
Explanation
The most suitable machine learning technique for generating captions for images is Supervised Learning. This approach leverages labeled data (image-caption pairs) to train the model to associate visual features with textual descriptions. Unsupervised Learning does not use labeled data, making it unsuitable for this task. Reinforcement Learning is more appropriate for tasks that involve feedback-based optimization rather than learning from labeled data. Semi-Supervised Learning could be considered if labeled data is limited, but it is not the primary choice when sufficient labeled data is available.
- A. Correct.
Supervised Learning is ideal because it requires labeled data, such as image-caption pairs, which is essential for training the model to understand the relationship between visual data (images) and textual data (captions).
- B. Incorrect.
Unsupervised Learning is not suitable in this case because it does not rely on labeled data, which is crucial for associating an image with its corresponding caption.
- C. Incorrect.
While Reinforcement Learning can be used for fine-tuning and optimizing models, it is not the primary approach for learning from labeled image-caption pairs.
- D. Incorrect.
Semi-Supervised Learning could be an option if labeled data is scarce, but it is not the best choice when sufficient labeled data is available for training.