Abstract: In an information processing method executed by an information processing device having a processor, the processor executes generating a first image in raster format using a generative adversarial network that includes a generator and a discriminator, converting the first image into a second image in vector format, inputting a third image obtained by converting the second image into raster format, or the second image, to the discriminator, and acquiring discrimination results, and storing the second image in a storage unit, if the discrimination results are true, or if the discrimination results are the same as discrimination results by the discriminator regarding the first image.

Abstract: Provided is a new mechanism enabling an appropriate distributed instance number or a hyperparameter to be specified with respect to a prescribed data set.

Abstract: It is intended to provide a significant data expansion algorithm for predetermined data. An information processing method performed by a processor included in an information processing device, the method includes: acquiring expanded data resulting from expansion of target data using an optional data expansion algorithm including a coupled function obtained by coupling together a plurality of data expandable functions by using weights; implementing learning, the learning including implementing the learning by inputting the expanded data to a learning model that performs predetermined learning and implementing the learning by using each item of the expanded data generated by stepwise changing a weight of the coupled function; specifying a boundary weight with which a learning result of the learning indicates an intended result and associating the boundary weight with information related to the target data.

Abstract: Provided are an analysis device, an analysis method, and an analysis program which predict the performance of a learning model when learning processing is executed using multiple algorithms. Using a predictive model produced by supervised learning using first shape information representing a global shape of a first loss function set for a prescribed problem and the performance of the learning model as learning data, an analysis device 10 predicts, for each of the multiple algorithms, the performance of a learning model when machine learning by the learning model is executed so that a second loss function has a reduced value on the basis of second shape information representing a global shape of the second loss function set for a new problem.

Abstract: An information processing apparatus includes a memory and processor. The memory stores a first inference model using a neural network and a plurality of defense algorithms. The at least one processor performs acquisition of prescribed data, input of the prescribed data to the first inference model to perform inference processing, the first inference model being learned using learning data including respective data and respective result data obtained by solving prescribed problems using the respective data, detection of a possibility as to whether a prescribed attack has been made on the prescribed data, specification of, when the possibility of the prescribed attack is detected, a first defense algorithm capable of making a defense against the prescribed attack from among the plurality of defense algorithms on a basis of the prescribed data on which the prescribed attack has been made, and application of the first defense algorithm to the inference processing.

Abstract: An anomaly of a subject can be properly determined even in false image recognition of the subject. An information processing method, performed by at least one processor in an information processing apparatus, comprises acquiring an image including a subject from an imaging apparatus; acquiring respectively items of data from a plurality of sensors provided for the subject or an object near the subject, the items of data being sensed respectively by the plurality of sensors; acquiring a determination result on whether the subject is abnormal or not by inputting the items of data acquired from the plurality of sensors to a learning model utilizing a neural network, wherein the learning model has learned presence or absence of an anomaly in the subject by using respectively past items of data from the plurality of sensors as learning data; and determining whether a recognition result of the subject based on the image is abnormal or not by using the determination result.

Abstract: To cause a more appropriate function to be applied to a hidden layer in a neural network.

Abstract: Provided are an analysis device, an analysis method, and an analysis program which predict the performance of a learning model when learning processing is executed using multiple algorithms. Using a predictive model produced by supervised learning using first shape information representing a global shape of a first loss function set for a prescribed problem and the performance of the learning model as learning data, an analysis device 10 predicts, for each of the multiple algorithms, the performance of a learning model when machine learning by the learning model is executed so that a second loss function has a reduced value on the basis of second shape information representing a global shape of the second loss function set for a new problem.

Abstract: Provided are an analysis device, an analysis method, and an analysis program which predict the performance of a learning model when learning processing is executed using multiple algorithms. Using a predictive model produced by supervised learning using first shape information representing a global shape of a first loss function set for a prescribed problem and the performance of the learning model as learning data, an analysis device 10 predicts, for each of the multiple algorithms, the performance of a learning model when machine learning by the learning model is executed so that a second loss function has a reduced value on the basis of second shape information representing a global shape of the second loss function set for a new problem.

Abstract: An information processing device includes: an index calculation unit configured to calculate an at least one index of one of actions and reactions by a target on the basis of target information acquired by an information acquisition unit; and a determination unit configured to determine a current psychological status of a plurality of psychological statuses of the target on the basis of the calculated at least one index. After the information acquisition unit acquires the target information, the index calculation unit updates the at least one index on the basis of the acquired target information. After the index calculation unit updates the at least one index, the determination unit determines, on the basis of the updated at least one index, that another one psychological status in a rank that is one of higher and lower than a rank of the current psychological status is a new current-psychological status.