The document describes a new framework called FUNIT for few-shot unsupervised image-to-image translation. FUNIT aims to map an image from a source class to an analogous target class using only a few examples of the target class provided during testing, without requiring examples of the target class during training. During training, FUNIT learns to translate between any two classes from a set of source classes, and during testing can translate an input to a never-before-seen target class by leveraging only a few examples of that new target class. FUNIT achieves this using an adversarial loss, content reconstruction loss, and feature matching loss.

1. Few-Shot Unsupervised Image-to-Image Translation
Ming-Yu Liu Xun Huang Arun Mallya Tero Karras Timo Aila Jaakko Lehtinen Jan Kautz
Bingwen Hu
2019-05-19

2. Problems
While unsupervised/unpaired image-to-image translation methods (e.g., Liu and Tuzel,
Liu et. al., Zhu et. al., and Huang et. al.) have achieved remarkable success, they are
still limited in two aspects.
• First, they generally require seeing a lot of images from target class in the training
time; generating poor translation outputs if only few images are given at training time
• Second, a trained model for a translation task cannot be repurposed for another
translation task in the test time, the learned models are limited for translating images
between two classes.

4. FUNIT
• The proposed FUNIT framework aims at mapping an image of a source
class to an analogous image of an unseen target class by leveraging a few
target class images that are made available at test time.
• In the training time, the FUNIT model learns to translate images between
any two classes sampled from a set of source classes. In the test time, the
model is presented a few images of a target class that the model has never
seen before. The model leverages these few example images to translate
an input image of a source class to the target class.

6. We assume the content image belongs to object class cx while each of the K
class images belong to object class cy. In general, K is a small number and cx
is different from cy.

8. where LGAN, LR, and LF are the GAN loss, the content image
reconstruction loss, and the feature matching loss.
Learning
GAN loss:
Total:
Content reconstruction loss:
Feature matching loss: