Abstract: A method of performing distributed matrix computation using task entanglement-based coding as a method of processing a huge amount of matrix computation in a distributed manner in a distributed computing environment is provided. A main server encodes information to be transmitted to a plurality of edge devices for distributed matrix computation on the basis of task entanglement-based coding employing a Chebyshev polynomial, thereby reducing the amount of information to be transmitted. Also, when the number of computation results received from the edge devices becomes a recovery threshold, the main server immediately performs decoding to derive a matrix computation result.

Abstract: There is provided a medical image segmentation deep-learning model generation apparatus including a training data generation/allocation unit configured to generate a training dataset through a segmentation result value acquired by inputting a given medical image to an original medical image segmentation deep-learning model and a learning control unit configured to acquire temporary weights using output data corresponding to primary learning by inputting good task data and bad task data sampled from primary learning training datasets to the medical image segmentation deep-learning model and configured to update weights by adding gradients acquired using weights acquired using output data corresponding to secondary learning by inputting good task data and bad task data sampled from secondary learning training datasets to the medical image segmentation deep-learning model, wherein the primary learning and the secondary learning are repeated.

Abstract: Provided is an apparatus for training a facial-locality super resolution deep neural network, the apparatus including a generator configured to receive a low-resolution image and convert the received low-resolution image into a fake high-resolution image similar to an original high-resolution image, a discriminator configured to compare the fake high-resolution image output from the generator with the original high-resolution image to determine authenticity, and a facial-locality loss term configured to calculate a loss that is to be minimized by the generator according to the authenticity output from the discriminator, wherein the generator is an artificial neural network learning model that learns while adjusting a weight to minimize the loss, and the facial-locality loss term calculates the loss of the generator by reflecting pixel information about a feature region of a face.

Abstract: There is provided an uncertainty prediction apparatus including an artificial neural network model trained based on deep learning, sampling models modeled by at least two weights obtained through sampling during a training process for the artificial neural network model, and an output generation unit configured to generate a result value reflecting an uncertainty degree by aggregating values output from the artificial neural network model and the sampling models after the same data is input to the artificial neural network model and the sampling models.

Abstract: There is provided a medical image segmentation deep-learning model generation apparatus including a training data generation/allocation unit configured to generate a training dataset through a segmentation result value acquired by inputting a given medical image to an original medical image segmentation deep-learning model and a learning control unit configured to acquire temporary weights using output data corresponding to primary learning by inputting good task data and bad task data sampled from primary learning training datasets to the medical image segmentation deep-learning model and configured to update weights by adding gradients acquired using weights acquired using output data corresponding to secondary learning by inputting good task data and bad task data sampled from secondary learning training datasets to the medical image segmentation deep-learning model, wherein the primary learning and the secondary learning are repeated.

Abstract: Provided is an apparatus for training a facial-locality super resolution deep neural network, the apparatus including a generator configured to receive a low-resolution image and convert the received low-resolution image into a fake high-resolution image similar to an original high-resolution image, a discriminator configured to compare the fake high-resolution image output from the generator with the original high-resolution image to determine authenticity, and a facial-locality loss term configured to calculate a loss that is to be minimized by the generator according to the authenticity output from the discriminator, wherein the generator is an artificial neural network learning model that learns while adjusting a weight to minimize the loss, and the facial-locality loss term calculates the loss of the generator by reflecting pixel information about a feature region of a face.