Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Generate Samples from Noise through Infusion Training   In this work, we investigate a novel training procedure to learn a generative
model as the transition operator of a Markov chain, such that, when applied
repeatedly on an unstructured random noise sample, it will denoise it into a
sample that matches the target distribution from the training set. The novel
training procedure to learn this progressive denoising operation involves
sampling from a slightly different chain than the model chain used for
generation in the absence of a denoising target. In the training chain we
infuse information from the training target example that we would like the
chains to reach with a high probability. The thus learned transition operator
is able to produce quality and varied samples in a small number of steps.
Experiments show competitive results compared to the samples generated with a
basic Generative Adversarial Net

2
The categories of this paper are: math
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The categories of this paper are: gr-qc
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The categories of this paper are: eess
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The categories of this paper are: adap-org
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
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The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
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The categories of this paper are: physics
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: nlin