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Few-Shot Diffusion Models
Giorgio Giannone · Didrik Nielsen · Ole Winther

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in NeurIPS 2022 Workshop on Score-Based Methods »
Event URL: https://openreview.net/forum?id=rqKTms-YHAW »

Denoising diffusion probabilistic models (DDPM) are powerful hierarchical latent variable models with remarkable sample generation quality and training stability. These properties can be attributed to parameter sharing in the generative hierarchy, as well as a parameter-free diffusion-based inference procedure. In this paper, we present Few-Shot Diffusion Models (FSDM), a framework for few-shot generation leveraging conditional DDPMs. FSDMs are trained to adapt the generative process conditioned on a small set of images from a given class by aggregating image patch information using a set-based Vision Transformer (ViT). At test time, the model is able to generate samples from previously unseen classes conditioned on as few as 5 samples from that class. We empirically show that FSDM can perform few-shot generation and transfer to new datasets. We benchmark variants of our method on complex vision datasets for few-shot learning and compare to unconditional and conditional DDPM baselines. Additionally, we show how conditioning the model on patch-based input set information improves training convergence.

Author Information

Giorgio Giannone (Technical University of Denmark - DTU)

Science is built up with data, as a house is with stones. But a collection of data is no more a science than a heap of stones is a house. (J.H. Poincaré)

Didrik Nielsen (DTU Compute)
Ole Winther (Technical University of Denmark)

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