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D2C is a VAE-based generative model suitable for few-shot conditional generation.
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D2C: Diffusion-Denoising Models for Few-shot Conditional Generation D2C: Diffusion-Denoising Models for Few-shot Conditional Generation Abhishek Sinha* 1 , Jiaming Song* 1 , Chenlin Meng 1 , Stefano Ermon 1 , 1 Stanford University arXiv Code D2C is a unconditional generative model for few-shot conditional generation. By learning from as few as 100 labeled examples, D2C can be used to generate images with a certain label or manipulate an existing image to contain a certain label. Abstract Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire. This paper describes Diffusion-Decoding models with Contrastive representations (D2C), a paradigm for training unconditional variational autoencoders (VAEs) for few-shot conditional image generation. D2C uses a learned diffusion-based prior over the latent...
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