Abstract: A system for active-training a neural network includes an input interface to receive a set of images, a memory to store an active sampler, a feature generator and a subset of the images selected from the set of the images, the subset of the images having similarity values based on a predetermined feature domain information, at least one processor to train the feature generator by use of the active sampler. The active sampler is configured to perform first-selecting, from the subset of the images, a pair of images as matching pair images according to a threshold of similarity values, second-selecting a pair of images from another subset of the images, feeding the matching pair images and the unmatched pair images to the feature generator, updating weighting parameters and increasing the threshold according to preset values an output interface to render the weighting parameters of the feature generator.

Abstract: A system for active-training a neural network includes an input interface to receive a set of images, a memory to store an active sampler, a feature generator and a subset of the images selected from the set of the images, the subset of the images having similarity values based on a predetermined feature domain information, at least one processor to train the feature generator by use of the active sampler. The active sampler is configured to perform first-selecting, from the subset of the images, a pair of images as matching pair images according to a threshold of similarity values, second-selecting a pair of images from another subset of the images, feeding the matching pair images and the unmatched pair images to the feature generator, updating weighting parameters and increasing the threshold according to preset values an output interface to render the weighting parameters of the feature generator.