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Consider a scenario where you're training a generative A1 model to create realistic images from text descriptions. You notice that the generated images lack fine-grained details and appear blurry. Which of the following loss functions or training techniques could you employ to improve the image quality and sharpness?
Correct Answer: B
Perceptual loss is specifically designed to capture high-level perceptual similarities between images, leading to sharper and more realistic outputs- MSE and L1 losses tend to produce blurry images as they penalize pixel-wise differences without considering perceptual similarity. Cross-entropy loss is relevant for classification tasks, not image generation directly from text descriptions. Increasing the batch size can improve training stability but does not directly address the lack of fine-grained details.