Deep generation of user-customized items
AssigneesAdobe Inc., The Regents of the University of California
InventorsChen Fang, Zhaowen Wang, Wangcheng Kang, Julian McAuley
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Abstract
The present disclosure relates to a personalized fashion generation system that synthesizes user-customized images using deep learning techniques based on visually-aware user preferences. In particular, the personalized fashion generation system employs an image generative adversarial neural network and a personalized preference network to synthesize new fashion items that are individually customized for a user. Additionally, the personalized fashion generation system can modify existing fashion items to tailor the fashion items to a user's tastes and preferences.
Claims20
A system for synthesizing user-customized images based on latent user preferences comprising:
- a memory component comprising:
- an image generative adversarial network that generates realistic images of items;
- and
- a preference predictor network that determines preferences of individual users;
- one or more processing devices coupled to the memory component, the one or more processing devices to perform operations comprising:
- identifying a latent code that causes the image generative adversarial network to generate an image that approximates a query image;
- determining an updated latent code optimized for a user based on the identified latent code and the preference predictor network by:
- iteratively searching for an additional latent code in an adjacent random latent space that yields a higher preference prediction score by the preference predictor network than the latent code;
- and
- selecting, as the updated latent code, the additional latent code that yields a highest preference prediction score for the user;
- generating, using the updated latent code and the image generative adversarial network, a realistic synthesized image of an item customized for the user;
- and
- providing the realistic synthesized image of the item customized for the user to a client device associated with the user.
A non-transitory computer-readable medium storing executable instructions which, when executed by a processing device, cause the processing device to perform operations comprising:
- identifying a latent code that causes an image generative adversarial network to generate an image that approximates a query image;
- determining an updated latent code optimized for a user based on the identified latent code, a preference predictor network, and the image generative adversarial network by:
- iteratively searching for an additional latent code in an adjacent random latent space that yields a higher preference prediction score by the preference predictor network than the latent code;
- and
- selecting, as the updated latent code, the additional latent code that yields a highest preference prediction score for the user;
- generating, using the updated latent code and the image generative adversarial network, a realistic synthesized image of an item customized for the user;
- and
- providing the realistic synthesized image of the item customized for the user to a client device associated with the user.
A method comprising:
- identifying a latent code that causes an image generative adversarial network to generate an image that approximates a query image;
- determining an updated latent code optimized for a user based on the identified latent code, utilizing a preference predictor network and the image generative adversarial network by:
- iteratively searching for an additional latent code in an adjacent random latent space that yields a higher preference prediction score by the preference predictor network than the latent code;
- and
- selecting, as the updated latent code, the additional latent code that yields a highest preference prediction score for the user;
- generating, using the updated latent code and the image generative adversarial network, a realistic synthesized image of an item customized for the user;
- and
- providing the realistic synthesized image of the item customized for the user to a client device associated with the user.
Dependencies are inferred from the claim text.