Abstract: Systems and methods provide a learned difference metric that operates in a wide artifact space. An example method includes initializing a committee of deep neural networks with labeled distortion pairs, iteratively actively learning a difference metric using the committee and psychophysics tasks for informative distortion pairs, and using the difference metric as an objective function in a machine-learned digital file processing task. Iteratively actively learning the difference metric can include providing an unlabeled distortion pair as input to each of the deep neural networks in the committee, a distortion pair being a base image and a distorted image resulting from application of an artifact applied to the base image, obtaining a plurality of difference metric scores for the unlabeled distortion pair from the deep neural networks, and identifying the unlabeled distortion pair as an informative distortion pair when the difference metric scores satisfy a diversity metric.

Abstract: Systems and methods provide a learned difference metric that operates in a wide artifact space. An example method includes initializing a committee of deep neural networks with labeled distortion pairs, iteratively actively learning a difference metric using the committee and psychophysics tasks for informative distortion pairs, and using the difference metric as an objective function in a machine-learned digital file processing task. Iteratively actively learning the difference metric can include providing an unlabeled distortion pair as input to each of the deep neural networks in the committee, a distortion pair being a base image and a distorted image resulting from application of an artifact applied to the base image, obtaining a plurality of difference metric scores for the unlabeled distortion pair from the deep neural networks, and identifying the unlabeled distortion pair as an informative distortion pair when the difference metric scores satisfy a diversity metric.

Abstract: Systems and methods provide a deep neural network trained via active learning. An example method includes generating, from a set of labeled objects, a plurality of differing training sets, assigning each of the plurality of training sets to a respective deep neural network in a committee of networks, and initializing each of the deep neural networks in the committee by training the deep neural network using the respective assigned training set. The method further includes iteratively training the deep neural networks in the committee until convergence and using one of the deep neural networks to make predictions for unlabeled objects. The training may include identifying unlabeled objects with highest diversity in predictions from the plurality of deep neural networks, obtaining a respective label for each identified unlabeled object, and retraining the deep neural networks with the respective labels for the objects.