Comparative analysis of adversarial and diffusion models for image generation
Abstract
The paper presents a formalized model of the image generation process within two architectural paradigms: generative adversarial networks and diffusion-based models. A quantitative comparison is conducted using inference step count and FLOPs estimation, accompanied by a qualitative analysis of training stability, mode collapse susceptibility, latent space interpretability, and scalability.
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