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.

Abstract: A method of generating a font database, and a method of training a neural network model are provided, which relate to a field of artificial intelligence, in particular to a computer vision and deep learning technology. The method of generating the font database includes: determining, by using a trained similarity comparison model, a basic font database most similar to handwriting font data of a target user in a plurality of basic font databases as a candidate font database; and adjusting, by using a trained basic font database model for generating the candidate font database, the handwriting font data of the target user, so as to obtain a target font database for the target user.

Abstract: A training method for a handwritten text image generation model includes: obtaining training data including a sample content image, a first sample handwritten text image and a second sample handwritten text image, constructing an initial training model; obtaining a first predicted handwritten text image by inputting the sample content image and the second sample handwritten text image into an initial handwritten text image generation model of the initial training model; obtaining a second predicted handwritten text image by inputting the sample content image and the first sample handwritten text image into an initial handwritten text image reconstruction model of the initial training model; training the initial training model according to the first and second predicted handwritten text images and the first sample handwritten text image; and determining a handwritten text image generation model of the training model after training as a target handwritten text image generation model.

Abstract: Provided is a training method for a character generation model. The training method for a character generation model includes: a first training sample is input into a target model to calculate a first loss, where the first training sample includes a first source domain sample word and a first target domain sample word, and content of the first source domain sample word is different from content of the first target domain sample word; a second training sample is input into the target model to calculate a second loss, where the second training sample includes a second source domain sample word and a second target domain sample word, content of the second source domain sample word is the same as content of the second target domain sample word; and a parameter of the character generation model is adjusted according to the first loss and the second loss.

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.

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.

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 and a preset target-domain sample character are input into a character classification model to obtain a first feature loss of the font generation model; the first target-domain generated character and the target-domain sample character are input into a font classification model to obtain a second feature loss of the font generation model; a target feature loss is determined according to the first feature loss and/or the second feature loss; and the model parameter of the font generation model is updated according to the target feature loss.

Abstract: Provided is a training method for a character generation model, and a character generation method, apparatus and device, which relates to the technical field of artificial intelligences, particularly, the technical field of computer vision and deep learning. The specific implementation schemes are: a source domain sample word and a target domain style word are input into the character generation model to obtain a target domain generation word; the target domain generation word and a target domain sample word are input into a pre-trained character classification model to calculate a feature loss of the character generation model; and a parameter of the character generation model is adjusted according to the feature loss.

Abstract: There are provided 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 technology, in particular to a field of computer vision and deep learning technologies. The method includes: generating a generated character based on a content character sample having a base font and a style character sample having a style font and generating a reconstructed character based on the content character sample, by using a generation model; calculating a basic loss of the generation model based on the generated character and the reconstructed character, by using a discrimination model; calculating a character loss of the generation model through classifying the generated character by using a trained character classification model; and adjusting a parameter of the generation model based on the basic loss and the character loss.

Abstract: A method of training a cycle generative networks model and a method of building a character library are provided, which relate to a field of artificial intelligence, in particular to a computer vision and deep learning technology, and which may be applied to a scene such as image processing and image recognition. A specific implementation scheme includes: inputting a source domain sample character into the cycle generative networks model to obtain a first target domain generated character; calculating a character error loss and a feature loss of the cycle generative networks model by inputting the first target domain generated character and a preset target domain sample character into a character classification model; and adjusting a parameter of the cycle generative networks model according to the character error loss and the feature loss. An electronic device and a storage medium are further provided.

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.

Abstract: A method of generating a font database, and a method of training a neural network model are provided, which relate to a field of artificial intelligence, in particular to a computer vision and deep learning technology. The method of generating the font database includes: determining, by using a trained similarity comparison model, a basic font database most similar to handwriting font data of a target user in a plurality of basic font databases as a candidate font database; and adjusting, by using a trained basic font database model for generating the candidate font database, the handwriting font data of the target user, so as to obtain a target font database for the target user.

Abstract: A method for training a font generation model is described below. A source domain sample character and a target domain association character are input into a font generation network to obtain a target domain generation character. The target domain generation character and at least one of a target domain sample character or the target domain association character are input into a loss analysis network to obtain a first loss, and a parameter of the font generation model is adjusted according to the first loss. The source domain sample character and a random vector are input into the font generation network to obtain a random domain generation character. The random domain generation character and a random domain sample character are input into the loss analysis network to obtain a second loss, and the parameter is readjusted according to the second loss.

Abstract: A method for training a font generation model is described below. A source domain sample character and a target domain association character are input into an encoder of the font generation model to obtain a sample character content feature and an association character style feature. The sample character content feature and the association character style feature are input into an attention mechanism network to obtain a target domain style feature. The sample character content feature and the target domain style feature are input into a decoder to obtain a target domain generation character. The target domain generation character and at least one of a target domain sample character or the target domain association character are input into a loss analysis network of the font generation model to obtain a model loss, and a parameter of the font generation model is adjusted according to the model loss.