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You're working on a multimodal AI system that combines text and image dat a. You're using a contrastive learning approach to learn joint embeddings of text and images. However, you notice that the system performs well on seen image-text pairs but poorly on unseen combinations. What technique MOST directly addresses this generalization problem?
Correct Answer: C
Hard negative mining focuses on selecting the most challenging negative examples (incorrect image-text pairs) during training. This forces the model to learn more robust and discriminative embeddings that generalize better to unseen combinations. Increasing embedding dimension or using larger batch size might help to some extent, but hard negative mining directly addresses the core issue of distinguishing similar but incorrect pairs. Decreasing the temperature parameter can make the contrastive loss too sensitive, potentially hindering generalization. A simpler model architecture may be detrimental if it lacks the capacity to capture the complex relationships