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You're analyzing the performance of a generative A1 model that produces images from text prompts. You notice that the model struggles to generate images with specific objects mentioned in the prompt, even though these objects appear frequently in the training dataset. Which of the following techniques could BEST address this issue?
Correct Answer: B
Re-weighting the training data to emphasize examples containing the underrepresented objects directly addresses the issue of the model not learning these objects effectively. Increasing batch size or adding layers might improve overall performance but doesn't specifically target the object representation problem. Reducing learning rate may lead to slower convergence. Increasing kernel size changes receptive field but does not address the imbalanced representation of objects.