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Abstract
Dream generation is an emerging field within artificial intelligence that aims to replicate the human experience of dreaming through computational models. This paper compares various AI algorithms used for dream generation, evaluating their performance, creativity, and computational efficiency. We explore Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models, providing a comprehensive analysis of their strengths and weaknesses. Our results indicate that each model has unique advantages, suggesting potential hybrid approaches for future research.
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