Learning Image-to-Image Translation Using Paired and Unpaired Training Samples

Soumya Tripathy*, Juho Kannala, Esa Rahtu

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

11 Sitaatiot (Scopus)


Image-to-image translation is a general name for a task where an image from one domain is converted to a corresponding image in another domain, given sufficient training data. Traditionally different approaches have been proposed depending on whether aligned image pairs or two sets of (unaligned) examples from both domains are available for training. While paired training samples might be difficult to obtain, the unpaired setup leads to a highly under-constrained problem and inferior results. In this paper, we propose a new general purpose image-to-image translation model that is able to utilize both paired and unpaired training data simultaneously. We compare our method with two strong baselines and obtain both qualitatively and quantitatively improved results. Our model outperforms the baselines also in the case of purely paired and unpaired training data. To our knowledge, this is the first work to consider such hybrid setup in image-to-image translation.

OtsikkoComputer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
ToimittajatKonrad Schindler, Greg Mori, C.V. Jawahar, Hongdong Li
ISBN (painettu)9783030208899
DOI - pysyväislinkit
TilaJulkaistu - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaAsian Conference on Computer Vision - Perth, Austraalia
Kesto: 2 jouluk. 20186 jouluk. 2018
Konferenssinumero: 14


NimiLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
KustantajaSpringer Nature
Vuosikerta11362 LNCS
ISSN (painettu)0302-9743
ISSN (elektroninen)1611-3349


ConferenceAsian Conference on Computer Vision


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