Diffusion
Generates data by learning to reverse a gradual noising process.
Resumen
A diffusion model learns to remove noise step by step, so that sampling starts from pure noise and arrives at a plausible sample. It solves the training instability of adversarial methods by replacing a two-player game with a single denoising objective. It exists because that objective is stable and covers the data distribution well, which is why it became the dominant approach for image generation.
Grafo de tecnologías
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Usa y depende de
- Neural Network concept Algorithms Diffusion based on Neural Network High ✓ source
Usado por
- Image Generation application Applications Diffusion used by Image Generation High ✓ source
Herramientas
Software que implementa o se integra con Diffusion.
Frameworks
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Comparativas
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