Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: One-Shot Unsupervised Cross Domain Translation   Given a single image x from domain A and a set of images from domain B, our
task is to generate the analogous of x in B. We argue that this task could be a
key AI capability that underlines the ability of cognitive agents to act in the
world and present empirical evidence that the existing unsupervised domain
translation methods fail on this task. Our method follows a two step process.
First, a variational autoencoder for domain B is trained. Then, given the new
sample x, we create a variational autoencoder for domain A by adapting the
layers that are close to the image in order to directly fit x, and only
indirectly adapt the other layers. Our experiments indicate that the new method
does as well, when trained on one sample x, as the existing domain transfer
methods, when these enjoy a multitude of training samples from domain A. Our
code is made publicly available at
https://github.com/sagiebenaim/OneShotTranslation

11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th