Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph-based Generative Face Anonymisation with Pose Preservation   We propose AnonyGAN, a GAN-based solution for face anonymisation which
replaces the visual information corresponding to a source identity with a
condition identity provided as any single image. With the goal to maintain the
geometric attributes of the source face, i.e., the facial pose and expression,
and to promote more natural face generation, we propose to exploit a Bipartite
Graph to explicitly model the relations between the facial landmarks of the
source identity and the ones of the condition identity through a deep model. We
further propose a landmark attention model to relax the manual selection of
facial landmarks, allowing the network to weight the landmarks for the best
visual naturalness and pose preservation. Finally, to facilitate the appearance
learning, we propose a hybrid training strategy to address the challenge caused
by the lack of direct pixel-level supervision. We evaluate our method and its
variants on two public datasets, CelebA and LFW, in terms of visual
naturalness, facial pose preservation and of its impacts on face detection and
re-identification. We prove that AnonyGAN significantly outperforms the
state-of-the-art methods in terms of visual naturalness, face detection and
pose preservation.

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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin