Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training End-to-end Single Image Generators without GANs   We present AugurOne, a novel approach for training single image generative
models. Our approach trains an upscaling neural network using non-affine
augmentations of the (single) input image, particularly including non-rigid
thin plate spline image warps. The extensive augmentations significantly
increase the in-sample distribution for the upsampling network enabling the
upscaling of highly variable inputs. A compact latent space is jointly learned
allowing for controlled image synthesis. Differently from Single Image GAN, our
approach does not require GAN training and takes place in an end-to-end fashion
allowing fast and stable training. We experimentally evaluate our method and
show that it obtains compelling novel animations of single-image, as well as,
state-of-the-art performance on conditional generation tasks e.g.
paint-to-image and edges-to-image.

9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
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The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin