Abstract: A method comprising: receiving a dataset comprising a plurality of data instances; extracting a feature vector representation of each of the data instances in the dataset; choosing a first data instance for adding to a subset of the dataset, wherein the first data instance is removed from the dataset; performing an iterative process comprising: (i) identifying one of the data instances in the dataset which represents a maximal information addition to the subset, based, at least in part, on measuring an information difference parameter between the feature vector representation of the identified data instance and the feature vector representations of all of the data instances in the subset, and (ii) adding the identified data instance to the subset and removing the identified data instance from the dataset, until the information difference parameter is lower than a predetermined threshold; and outputting the subset as a representative subset of the dataset.

Abstract: A method comprising: receiving a dataset comprising a plurality of data instances; extracting a feature vector representation of each of the data instances in the dataset; choosing a first data instance for adding to a subset of the dataset, wherein the first data instance is removed from the dataset; performing an iterative process comprising: (i) identifying one of the data instances in the dataset which represents a maximal information addition to the subset, based, at least in part, on measuring an information difference parameter between the feature vector representation of the identified data instance and the feature vector representations of all of the data instances in the subset, and (ii) adding the identified data instance to the subset and removing the identified data instance from the dataset, until the information difference parameter is lower than a predetermined threshold; and outputting the subset as a representative subset of the dataset.

Abstract: A method comprising: receiving a dataset comprising (i) a plurality of images, and (ii) a set of classes associated with one or more objects in the images; selecting at least one image from the dataset; applying one or more transformations to the selected image, to create a set of transformed images, wherein each of the transformed images includes a representation of at least one of the objects; annotating at least some of the objects in the selected image and at least some of the transformed images, wherein the annotating comprises assigning each of the one or more objects to one of the classes; and calculating an ambiguity score with respect to at least one pair of classes in the set of classes, based, at least in part, on a number of times the annotating assigned a one of the objects to both of the classes in the pair.