Complexity-based progressive training for machine vision models

Abstract

Methods and systems for training machine vision models (MVMs) with 'noisy' training datasets are described. A noisy set of images is received, where labels for some of the images are 'noisy' and/or incorrect. A progressively-sequenced learning curriculum is designed for the noisy dataset, where the images that are easiest to learn machine-vision knowledge from are sequenced near the beginning of the curriculum and images that are harder to learn machine-vision knowledge from are sequenced later in the curriculum. An MVM is trained via providing the sequenced curriculum to a supervised learning method, so that the MVM learns from the easiest examples first and the harder training examples later, i.e., the MVM progressively accumulates knowledge from simplest to most complex. To sequence the curriculum, the training images are embedded in a feature space and the “complexity” of each image is determined via density distributions and clusters in the feature space.