DeepAI AI Chat
Log In Sign Up

Augmentation-Interpolative AutoEncoders for Unsupervised Few-Shot Image Generation

by   Davis Wertheimer, et al.

We aim to build image generation models that generalize to new domains from few examples. To this end, we first investigate the generalization properties of classic image generators, and discover that autoencoders generalize extremely well to new domains, even when trained on highly constrained data. We leverage this insight to produce a robust, unsupervised few-shot image generation algorithm, and introduce a novel training procedure based on recovering an image from data augmentations. Our Augmentation-Interpolative AutoEncoders synthesize realistic images of novel objects from only a few reference images, and outperform both prior interpolative models and supervised few-shot image generators. Our procedure is simple and lightweight, generalizes broadly, and requires no category labels or other supervision during training.


page 18

page 19

page 20

page 21

page 22

page 23

page 24

page 25


FIGR: Few-shot Image Generation with Reptile

Generative Adversarial Networks (GAN) boast impressive capacity to gener...

D2C: Diffusion-Denoising Models for Few-shot Conditional Generation

Conditional generative models of high-dimensional images have many appli...

DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta

Learning to generate new images for a novel category based on only a few...

F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation

In order to generate images for a given category, existing deep generati...

Variational Autoencoders Without the Variation

Variational autoencdoers (VAE) are a popular approach to generative mode...

Image Generation With Neural Cellular Automatas

In this paper, we propose a novel approach to generate images (or other ...

Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation

The performances of defect inspection have been severely hindered by ins...