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11.
公开(公告)号:US20220180043A1
公开(公告)日:2022-06-09
申请号:US17682422
申请日:2022-02-28
Inventor: Licheng TANG , Jiaming LIU
IPC: G06F40/109 , G06V30/19
Abstract: Provided is a training method for a character generation model, a character generation method, apparatus and device, which relate to the technical field of artificial intelligences, particularly, the technical field of computer vision and deep learning. The specific implementation scheme includes: a first training sample is acquired, a target model is trained based on the first training sample, and a first character confrontation loss is acquired; a second training sample is acquired, the target model is trained based on the second training sample, and a second character confrontation loss, a component classification loss and a style confrontation loss are acquired; and a parameter of the character generation model is adjusted according to the first character confrontation loss, the second character confrontation loss, the component classification loss and the style confrontation loss.
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12.
公开(公告)号:US20230114293A1
公开(公告)日:2023-04-13
申请号:US17682131
申请日:2022-02-28
Inventor: Jiaming LIU , Licheng TANG
IPC: G06V30/244 , G06V30/19 , G06T11/20
Abstract: Provided are a method for training a font generation model, a method for establishing a font library, and a device. The method for training a font generation model includes the following steps. A source-domain sample character is input into the font generation model to obtain a first target-domain generated character. The first target-domain generated character is input into a font recognition model to obtain the target adversarial loss of the font generation model. The model parameter of the font generation model is updated according to the target adversarial loss.
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公开(公告)号:US20220270384A1
公开(公告)日:2022-08-25
申请号:US17683945
申请日:2022-03-01
Inventor: Jiaming LIU , Zhibin HONG , Licheng TANG
Abstract: The present disclosure discloses a method for training an adversarial network model, a method for building a character library, an electronic device and a storage medium, which relate to a field of artificial intelligence, in particular to a field of computer vision and deep learning technologies, and are applicable in a scene of image processing and image recognition. The method for training includes: generating a new character by using the generation model based on a stroke character sample and a line character sample; discriminating a reality of the generated new character by using the discrimination model; calculating a basic loss based on the new character and a discrimination result; calculating a track consistency loss based on a track consistency between the line character sample and the new character; and adjusting a parameter of the generation model according to the basic loss and the track consistency loss.
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