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Awesome Few-Shot Image Generation Awesome

A curated list of resources including papers, datasets, and relevant links pertaining to few-shot image generation. Since few-shot image generation is a very broad concept, there are various experimental settings and research lines in the realm of few-shot image generation.

From Base Categories to Novel Categories

The generative model is trained on base categories and applied to novel categories with (optimization-based) or without finetuning (fusion-based and transformation-based).

Optimization-based methods:

Fusion-based methods:

Transformation-based methods:

Datasets:

From Large Dataset to Small Dataset

The generative model is trained on a large dataset (base domain/category) and transferred to a small dataset (novel domain/category).

Finetuning-based methods: Only finetune a part of the model parameters or train a few additional parameters.

Regularization-based methods: Regularize the transfer process based on the prior regularization knowledge, usually by imposing penalty on parameter/feature changes.

Datasets: Sometimes a subset of a dataset is used as the target dataset.

Only Small Dataset

The generative model is directly trained on a small dataset.

In the extreme case, the generative model is directly trained on a single image. However, the learnt model generally only manipulates the repeated patterns in this image.