Research and Optimization of Deep Learning-Based Image Generation Algorithms
Abstract
and optimization strategies. First, a comprehensive comparison of mainstream deep learning image generation models is presented, along with
relevant evaluation metrics. Next, detailed analyses of Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models in image generation applications and innovations are provided. Finally, methods for network structure optimization, loss function
improvement, and training strategy optimization are proposed to enhance the performance and stability of image generation algorithms. The
research demonstrates that optimizing algorithm structures and training methods can significantly improve the quality and diversity of generated images, offering new possibilities for computer vision, AI-driven artistic creation, and other fields.
Keywords
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DOI: http://dx.doi.org/10.70711/aitr.v2i10.7131
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