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 id : |
916
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| type : |
MSc_Thesis
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| dateandtime : |
2019-12-13 09:30:00
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| duration : |
90 min.
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Recommended duration for PhD thesis is 90 minutes, for other seminar types, it is 60 minutes. The duration specified here is used to reserve the room.
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| place : |
A105
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| departmental : |
yes
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| title : |
Learning an Embedding Space for All Modalities
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| author : |
OGUL CAN ERYUKSEL
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| supervisors : |
ASSOC.PROF.DR.SINAN KALKAN
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Supervisors field is applicable especially for a Thesis Defense
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| company : |
Computer Engineering Dept. Middle East Technical Univ.
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| country : |
Turkey
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| abstract : |
Thanks to advances in deep learning, striking results have been obtained in translation between different image modalities or spaces; e.g. using Generative Adversarial Networks, one can create a highly realistic colorized image of a black and white image, or daylight version of a nightlight image. However, existing studies generally tackle the problem in pairs and therefore, ignore the common information that are shared across different image modalities. In this thesis, a method that can create an embedding space shared by all different image modalities is proposed. The embedding space is constructed by employing pairs of modalities. Such a modality allows extracting a scene representation that is shared by all image modalities. Once learned, the space allows making zero-shot translations between two modalities for which paired data is not available. Moreover, a new modality can be easily integrated into the model easily, making it scalable.
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| notificationSent : |
final
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