Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Asymmetric Cycle-Consistency Loss for Dealing with Many-to-One
  Mappings in Image Translation: A Study on Thigh MR Scans   Generative adversarial networks using a cycle-consistency loss facilitate
unpaired training of image-translation models and thereby exhibit a very high
potential in manifold medical applications. However, the fact that images in
one domain potentially map to more than one image in another domain (e.g. in
case of pathological changes) exhibits a major challenge for training the
networks. In this work, we offer a solution to improve the training process in
case of many-to-one mappings by modifying the cycle-consistency loss. We show
formally and empirically that the proposed method improves the performance
significantly without radically changing the architecture and without
increasing the overall complexity. We evaluate our method on thigh MRI scans
with the final goal of segmenting the muscle in fat-infiltrated patients' data.

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The categories of this paper are: nlin
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The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-th
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The categories of this paper are: math
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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin