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You are training a Generative Adversarial Network (GAN) to generate realistic images. After several epochs, you observe that the generator is consistently producing similar images, regardless of the input noise. This phenomenon is known as:
Correct Answer: A
Mode collapse occurs when the generator in a GAN learns to produce a limited variety of outputs, often focusing on the most easily generated examples. This leads to a lack of diversity in the generated images. Vanishing and exploding gradients are related to the training process itself, overfitting relates to model complexity compared to the training data, and underfitting relates to insufficient model capacity.