Abstract: It is possible to identify a position of a target object that is difficult to recognize. A position estimation device includes: an information fusion unit that generates fusion information in which position information of a subject object that is an object corresponding to a subject, visual information of the subject object, and relationship information indicating a relationship with a target object paired with the subject object are fused; and an object position estimation unit that estimates a position of the target object by using an object position estimator learned in advance on the basis of the fusion information.

Abstract: There is provided a class label estimation device that estimates a class label of input data and estimates a cause of an estimation error, the class label estimation device including: a distribution estimation unit that estimates a distribution followed by a training set; a distance estimation unit that estimates a distance of the input data from the training set based on the distribution; an unknown degree estimation unit that estimates an unknown degree of the input data based on the distance; an unknown degree correction unit that corrects the unknown degree based on the distribution; and an error cause estimation unit that estimates a cause of an estimation error using the corrected unknown degree.

Abstract: A learning apparatus includes: a data generation unit that learns generation of data based on a class label signal and a noise signal; an unknown degree estimation unit that learns estimation of a degree to which input data is unknown using a training set and the data generated by the data generation unit; a first class likelihood estimation unit that learns estimation of a first likelihood of each class label for input data using the training set; a second class likelihood estimation unit that learns estimation of a second likelihood of each class label for input data using the training set and the data generated by the data generation unit; a class likelihood correction unit that generates a third likelihood by correcting the first likelihood on the basis of the unknown degree and the second likelihood; and a class label estimation unit that estimates a class label of data related to the third likelihood on the basis of the third likelihood, thereby automatically estimating a cause of an error by a deep mod

Abstract: Simultaneous learning of a plurality of different tasks and domains, with low costs and high precision, is enabled. A learning unit 160, on the basis of learning data, uses a target encoder that takes data of a target domain as input and outputs a target feature expression, a source encoder that takes data of a source domain as input and outputs a source feature expression, a common encoder that takes data of the target domain or the source domain as input and outputs a common feature expression, a target decoder that takes output of the target encoder and the common encoder as input and outputs a result of executing a task with regard to data of the target domain, and a source decoder that takes output of the source encoder and the common encoder as input and outputs a result of executing a task with regard to data of the source domain, to learn so that the output of the target decoder matches training data, and the output of the source decoder matches training data.

Abstract: A retrieval apparatus includes a first retrieval unit, a second retrieval unit, and an integration unit that calculates an integrated similarity by integrating a first similarity calculated by the first retrieval unit and a second similarity calculated by the second retrieval unit. For similarities between a basic image, an image similar to the basic image, and an image dissimilar to the basic image of the reference images, at least the feature extraction of the first retrieval unit is learned such that a margin based on a second similarity between the basic image and the similar image and a second similarity between the basic image and the dissimilar image increases as the second similarity between the basic image and the dissimilar image increases relative to the second similarity between the basic image and the similar image.

Abstract: A retrieval apparatus includes a first retrieval unit, a second retrieval unit, and an integration unit that calculates an integrated similarity by integrating a first similarity calculated by the first retrieval unit and a second similarity calculated by the second retrieval unit. For similarities between a basic image, an image similar to the basic image, and an image dissimilar to the basic image of the reference images, at least the feature extraction of the first retrieval unit is learned such that a margin based on a second similarity between the basic image and the similar image and a second similarity between the basic image and the dissimilar image increases as the second similarity between the basic image and the dissimilar image increases relative to the second similarity between the basic image and the similar image.

Abstract: Simultaneous learning of a plurality of different tasks and domains, with low costs and high precision, is enabled. A learning unit 160, on the basis of learning data, uses a target encoder that takes data of a target domain as input and outputs a target feature expression, a source encoder that takes data of a source domain as input and outputs a source feature expression, a common encoder that takes data of the target domain or the source domain as input and outputs a common feature expression, a target decoder that takes output of the target encoder and the common encoder as input and outputs a result of executing a task with regard to data of the target domain, and a source decoder that takes output of the source encoder and the common encoder as input and outputs a result of executing a task with regard to data of the source domain, to learn so that the output of the target decoder matches training data, and the output of the source decoder matches training data.