AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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An efficient diffusion framework that jointly diffuses a baseline scan and its follow-up residual, summed to synthesize the follow-up scan, while concurrently predicting a spatial uncertainty map, in a single reverse diffusion process is proposed.
It is proved that automatically generated domain-specific datasets lead to better pretraining than large-scale supervised datasets such as ImageNet-1k and ImageNet-21k and proves the superiority of PaS datasets over existing traditional domain-specific datasets in terms of diversity, scale, and effectiveness.
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