Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Metappearance: Meta-Learning for Visual Appearance Reproduction   There currently are two main approaches to reproducing visual appearance
using Machine Learning (ML): The first is training models that generalize over
different instances of a problem, e.g., different images from a dataset. Such
models learn priors over the data corpus and use this knowledge to provide fast
inference with little input, often as a one-shot operation. However, this
generality comes at the cost of fidelity, as such methods often struggle to
achieve the final quality required. The second approach does not train a model
that generalizes across the data, but overfits to a single instance of a
problem, e.g., a flash image of a material. This produces detailed and
high-quality results, but requires time-consuming training and is, as mere
non-linear function fitting, unable to exploit previous experience. Techniques
such as fine-tuning or auto-decoders combine both approaches but are sequential
and rely on per-exemplar optimization. We suggest to combine both techniques
end-to-end using meta-learning: We over-fit onto a single problem instance in
an inner loop, while also learning how to do so efficiently in an outer-loop
that builds intuition over many optimization runs. We demonstrate this concept
to be versatile and efficient, applying it to RGB textures, Bi-directional
Reflectance Distribution Functions (BRDFs), or Spatially-varying BRDFs
(svBRDFs).

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The categories of this paper are: physics
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The categories of this paper are: supr-con
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: acc-phys
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The categories of this paper are: stat
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
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The categories of this paper are: funct-an
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The categories of this paper are: eess