Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and
  Image-to-Image Translation from Unpaired Supervision   Researchers have developed excellent feed-forward models that learn to map
images to desired outputs, such as to the images' latent factors, or to other
images, using supervised learning. Learning such mappings from unlabelled data,
or improving upon supervised models by exploiting unlabelled data, remains
elusive. We argue that there are two important parts to learning without
annotations: (i) matching the predictions to the input observations, and (ii)
matching the predictions to known priors. We propose Adversarial Inverse
Graphics networks (AIGNs): weakly supervised neural network models that combine
feedback from rendering their predictions, with distribution matching between
their predictions and a collection of ground-truth factors. We apply AIGNs to
3D human pose estimation and 3D structure and egomotion estimation, and
outperform models supervised by only paired annotations. We further apply AIGNs
to facial image transformation using super-resolution and inpainting renderers,
while deliberately adding biases in the ground-truth datasets. Our model
seamlessly incorporates such biases, rendering input faces towards young, old,
feminine, masculine or Tom Cruise-like equivalents (depending on the chosen
bias), or adding lip and nose augmentations while inpainting concealed lips and
noses.

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The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: math-ph
23
The categories of this paper are: solv-int