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You are building a Generative Adversarial Network (GAN) to generate high-resolution images. The generated images suffer from mode collapse, where the generator only produces a limited variety of images. Which of the following techniques would be MOST effective in mitigating mode collapse?
Correct Answer: C
Mini-batch discrimination and feature matching encourage the generator to produce diverse images by considering the relationships between generated samples in a batch. Adjusting learning rates may have some effect but doesn't directly address the diversity issue. Reducing the latent space or using a simpler generator would likely reduce the generator's capacity and exacerbate mode collapse.