Curriculum learning strategies refer to structured approaches that enhance the training process of machine learning models by presenting data in a meaningful sequence. This methodology focuses on gradually introducing more complex tasks and examples, allowing models to learn more effectively by building upon previously acquired knowledge. By organizing the learning experience, these strategies can lead to improved performance and faster convergence, especially in challenging environments like quantum generative adversarial networks.
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