Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CoPE: Conditional image generation using Polynomial Expansions   Generative modeling has evolved to a notable field of machine learning. Deep
polynomial neural networks (PNNs) have demonstrated impressive results in
unsupervised image generation, where the task is to map an input vector (i.e.,
noise) to a synthesized image. However, the success of PNNs has not been
replicated in conditional generation tasks, such as super-resolution. Existing
PNNs focus on single-variable polynomial expansions which do not fare well to
two-variable inputs, i.e., the noise variable and the conditional variable. In
this work, we introduce a general framework, called CoPE, that enables a
polynomial expansion of two input variables and captures their auto- and
cross-correlations. We exhibit how CoPE can be trivially augmented to accept an
arbitrary number of input variables. CoPE is evaluated in five tasks
(class-conditional generation, inverse problems, edges-to-image translation,
image-to-image translation, attribute-guided generation) involving eight
datasets. The thorough evaluation suggests that CoPE can be useful for tackling
diverse conditional generation tasks. The source code of CoPE is available at
\url{https://github.com/grigorisg9gr/polynomial_nets_for_conditional_generation}.

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The categories of this paper are: funct-an
16
The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th