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Consider a scenario where you are building an autoencoder using a U-Net architecture. What loss function is generally considered MOST suitable for training this autoencoder, particularly when the goal is to generate high-quality images?
Correct Answer: B
Mean Squared Error (MSE) loss is commonly used for training autoencoders, including those based on IJ-Net architectures, when the goal is to reconstruct images. MSE measures the average squared difference between the original and reconstructed images. While SSIM focuses on structural similarity, MSE provides a more direct pixel-wise comparison. Cross-entropy and binary cross-entropy are more suitable for classification tasks.