Systems and method for assessing annotators by way of annotated images annotated by said annotators. Agent or annotator model modules are trained using annotated images annotated by specific annotators. A baseline model module is also trained using all of the annotated images used in training the agent model modules. The trained agent model modules are then used to annotate an evaluation dataset to result in evaluation result annotated images. The trained baseline model module is also used to annotate the evaluation dataset to result in its own evaluation result annotated images. The evaluation results from the agent model modules are compared with the evaluation result from the baseline model module. Based on the comparison results, scores are allocated to each agent model module. The scores are used to group agent model modules and annotators that correspond to the low scoring agent model modules can be targeted for retraining.

An apparatus in a network of apparatuses includes a first processing unit that has: a first measurement unit configured to receive items and take physical measurements, a first memory storing parameters that are useful for categorizing the items based on the physical measurements taken from the items and characteristics calculated using the physical measurements, and a first processing module including an artificial intelligence program. The first processing module automatically selects a source from which to receive new parameters based on similarity between physical measurements taken by the first processing unit and physical measurements that were taken by the sources, automatically modifies at least some of the parameters that are stored in the first memory with the new parameters received from the source and with measurements taken by the first processing unit to generate modified parameters, and transmitting a subset of the modified parameters to one or more recipients.

A machine learning model fraud detection system and fraud detection method wherein a license/model management apparatus: generates a test data-trained model by inputting a pre-trained model and test data associated therewith from a licensor apparatus, carrying out learning using the test data on the pre-trained model; stores the test data-trained model in association with the output values obtained when the test data is executed in the test data-trained model; inputs the associated test data into a user model, executes the model when the user model is inputted from a user apparatus using the test data-trained model; compares the output data from the user model with the stored output values from the test data-trained model and detects the fraud if the resulting error is outside tolerance limits.

Disclosed herein include systems and methods for biological object tracking and lineage construction. Also disclosed herein include cloud-based systems and methods for allocating computational resources for deep learning-enabled image analysis of biological objects. Also disclosed herein include systems and methods for annotating and curating biological object tracking-specific training datasets.

A method for optimizing a data model is used in a device. The device acquires data information and selecting at least two data models according to the data information, and utilizes the data information to train the at least two data models. The device acquires each accuracy of the at least two data models, determines a target data model which has greatest accuracy between the at least two data models, and optimizes the target data model.

A method and apparatus provide a virtual clothing wearing service based on deep-learning. A virtual clothing wearing server based on deep-learning includes a communicator configured to receive a user image and a v clothing image; a memory configured to store a program including first and second deep-learning models; a processor configured to generate an image of virtually dressing a virtual wearing clothing on a user. The program is configured to: generate, by the first deep-learning model, a transformed virtual wearing clothing image by transforming the virtual wearing clothing image in accordance with a body of the user in the user image based on the user image and the virtual wearing clothing image, and generate, by the second deep-learning model, the virtual wearing person image by dressing the transformed virtual wearing clothing on the body of the user based on the user image and the transformed virtual wearing clothing image.

The application relates to the technical field of artificial intelligence, and provides a method, device, electronic equipment and storage medium for positioning macular center in fundus images. The method comprises: acquiring a detection result of the fundus image detection model, wherein the detection result includes an optic disc area, and a first detection block and a first confidence score corresponding to the optic disc area, and a macular area, and a second detection block and a second confidence score corresponding to the macular area; calculating a center point coordinate of the optic disc area according to the first detection block, and calculating a center point coordinate of the macular area according to the second detection block; identifying whether the to-be-detected fundus image is a left eye fundus image or a right eye fundus image, and correcting a center point of the macular area using different correction models.

The application relates to the technical field of artificial intelligence, and provides a method, device, electronic equipment and storage medium for positioning macular center in fundus images. The method comprises: acquiring a detection result of the fundus image detection model, wherein the detection result includes an optic disc area, and a first detection block and a first confidence score corresponding to the optic disc area, and a macular area, and a second detection block and a second confidence score corresponding to the macular area; calculating a center point coordinate of the optic disc area according to the first detection block, and calculating a center point coordinate of the macular area according to the second detection block; identifying whether the to-be-detected fundus image is a left eye fundus image or a right eye fundus image, and correcting a center point of the macular area using different correction models.

This disclosure relates to a system for performing efficient learning of a specific portion. To achieve this purpose, there is proposed a system configured to generate a converted image on the basis of input of an input image, the system comprising a learning model in which parameters are adjusted so as to suppress an error between the input image and a second image converted upon input of the input image, the learning model being subjected to different learning at least between a first area in the image and a second area different from the first area.

This disclosure relates to a system for performing efficient learning of a specific portion. To achieve this purpose, there is proposed a system configured to generate a converted image on the basis of input of an input image, the system comprising a learning model in which parameters are adjusted so as to suppress an error between the input image and a second image converted upon input of the input image, the learning model being subjected to different learning at least between a first area in the image and a second area different from the first area.