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Few-Shot Unsupervised Image-to-Image Translation
Ming-Yu Liu Xun Huang Arun Mallya Tero Karras Timo Aila Jaakko Lehtinen Jan Kautz
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.
• 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.
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.
where LGAN, LR, and LF are the GAN loss, the content image
reconstruction loss, and the feature matching loss.
Content reconstruction loss:
Feature matching loss: