Abstract: According to one embodiment, a data analysis apparatus includes processing circuitry. The processing circuitry acquires a plurality of pieces of first data satisfying a first condition, generates a plurality of first feature vectors by unsupervised learning of the plurality of pieces of first data, generates a first clustering result by clustering the plurality of first feature vectors, acquires a plurality of pieces of second data satisfying a second condition different from the first condition, generates a plurality of second feature vectors by unsupervised learning of at least some of the plurality of pieces of first data and the plurality of pieces of second data, generates a second clustering result by clustering the second feature vectors, and generates a comparison result regarding the plurality of pieces of first data and the plurality of pieces of second data by comparing the first clustering result with the second clustering result.

Abstract: A similar image set creating apparatus includes processing circuitry. The processing circuitry acquires a plurality of images. The processing circuitry extracts first features from the images by using a first model that executes an image classification task. The processing circuitry extracts second features from the images by using a second model that executes an image classification task. The second model is trained in such a manner that mutually similar images in a latent space are continuously distributed, compared to the first model. The processing circuitry selects, from the images, an image of interest serving as a reference of a similar image set, and an auxiliary image similar to the image of interest, based on the first features and the second features.

Abstract: According to an embodiment, a learning device includes one or more processors. The processors calculate a latent vector of each of a plurality of first target data, by using a parameter of a learning model configured to output a latent vector indicating a feature of a target data. The processors calculate, for each first target data, first probabilities that the first target data belongs to virtual classes on an assumption that the plurality of first target data belong to the virtual classes different from each other. The processors update the parameter such that a first loss of the first probabilities, and a second loss that is lower as, for each of element classes to which a plurality of elements included in each of the plurality of first target data belong, a relation with another element class is lower, become lower.

Abstract: According to one embodiment, a data labeling work support apparatus includes a processor including hardware. The processor acquires a first label assigned to data. The processor acquires the data. The processor extracts a feature of the data. The processor groups the data based on a similarity or a distance of the feature. The processor assigns a second label to the grouped data. The processor calculates a degree of matching between the first label and the second label. The processor outputs information regarding a combination of the first label and the second label having a low degree of matching.

Abstract: According to an embodiment, a learning device includes one or more processors. The processors calculate a latent vector of each of a plurality of first target data, by using a parameter of a learning model configured to output a latent vector indicating a feature of a target data. The processors calculate, for each first target data, first probabilities that the first target data belongs to virtual classes on an assumption that the plurality of first target data belong to the virtual classes different from each other. The processors update the parameter such that a first loss of the first probabilities, and a second loss that is lower as, for each of element classes to which a plurality of elements included in each of the plurality of first target data belong, a relation with another element class is lower, become lower.

Abstract: According to one embodiment, a communication device includes one or more processors. The processors decide on a first-type parameter representing a conversion operation for converting input data into first-type conversion data. The processors calculate first-type predicted distributions based on second-type parameters, each representing one of the plurality of clusters. The processors update the first-type parameter and the second-type parameters so as to achieve optimization of first-type differences representing differences between the first-type predicted distributions and a target distribution, and second-type differences representing differences between the first-type predicted distributions and second-type predicted distributions that indicate probability at which second-type converted data. The second-type converted data is obtained by converting data, which is formed by augmentation of the input data, using the first-type parameter belongs to the clusters.

Abstract: According to one embodiment, a communication device includes one or more processors. The processors decide on a first-type parameter representing a conversion operation for converting input data into first-type conversion data. The processors calculate first-type predicted distributions based on second-type parameters, each representing one of the plurality of clusters. The processors update the first-type parameter and the second-type parameters so as to achieve optimization of first-type differences representing differences between the first-type predicted distributions and a target distribution, and second-type differences representing differences between the first-type predicted distributions and second-type predicted distributions that indicate probability at which second-type converted data. The second-type converted data is obtained by converting data, which is formed by augmentation of the input data, using the first-type parameter belongs to the clusters.

Abstract: According to one embodiment, a viewing log analysis device includes a hardware processor. The hardware processor is configured to acquire a viewing log for each device of a plurality of devices, the viewing log including identification of a day when the broadcast program is viewed and a time zone, generate viewing feature information including a viewing feature amount with respect to each of the plurality of devices, generate a viewing group including a set of devices from the plurality of devices based on similarity or distance between the viewing feature information, and display the viewing habit in the viewing group on the basis of the viewing feature amount with respect to each of the devices from the set of devices in the viewing group.