Abstract: An information processing device includes, in a case where a selection of a first photography point icon that is one of a plurality of photography point icons is detected, displaying a first image corresponding to the first photography point icon on a display, in which the displaying of the first image on the display includes determining whether annotation information is associated with a second photography point icon that is within a predetermined range from the first photography point icon, and upon determination that the annotation information is associated with the second photography point icon, displaying the first image and the annotation information on the display.

Abstract: An information processing device displays, on a display of an information terminal, a first image obtained by photographing a predetermined space on first date and time; and when detecting an instruction to select second date and time different from the first date and time, displays, on the display, a second image obtained by photographing the predetermined space on the second date and time, in which the second image is displayed on the display while having a default viewpoint being changed so as to match a viewpoint of the first image.

Abstract: An anomaly detection method by which a computer performs anomaly detection includes: obtaining first feature data outputted through N (N is an integer not less than 1) convolutional layers of a convolutional neural network configured as an encoder when an image is inputted to the convolutional neural network; obtaining second feature data outputted through M (M is an integer not less than 1, and M?N) convolutional layers of the convolutional neural network and different in size from the first feature data; and performing anomaly detection on the image by using features indicated by the first feature data and the second feature data that are different in size.

Abstract: First optimization processing for optimizing parameters of a DNN and second optimization processing for optimizing hyperpararneters for each sample used in data augmentation processing are alternately performed. The first optimization processing includes causing the DNN to predict a first augmentation label from a first augmented sample, calculating a first error function between the first augmentation label and a first correct label for a first sample, and updating the parameters in accordance with the first error function. The second optimization processing includes acquiring a second sample, causing the DNN after the updating of the parameters to predict a second label from the second sample, calculating a second error function between the second label and a second correct label for the second sample, and updating the hyperparameter in accordance with a gradient obtained by differentiation of the second error function with respect to the hyperparameter.